# Distillation and Stripping purification plants for JUNO liquid scintillator

C. Landini<EMAIL_ADDRESS>M. Beretta  P. Lombardi  A. Brigatti
M. Montuschi  S. Parmeggiano  G. Ranucci  V. Antonelli  D. Basilico  B.
Caccianiga  M.G. Giammarchi  L. Miramonti  E. Percalli  A.C. Re  P. Saggese
M.D.C. Torri  S. Aiello  G. Andronico  A. Barresi  A. Bergnoli  M. Borghesi
R. Brugnera  R. Bruno  A. Budano  A. Cammi  V. Cerrone  R. Caruso  D. Chiesa
C. Clementi  S. Dusini  A. Fabbri  G. Felici  A. Garfagnini  N. Giudice  A.
Gavrikov  M. Grassi  R.M. Guizzetti  N. Guardone  B. Jelmini  L. Lastrucci  I.
Lippi  L. Loi  C. Lombardo  F. Mantovani  S.M. Mari  A. Martini  M. Nastasi
D. Orestano  F. Ortica  A. Paoloni  F. Petrucci  E. Previtali  M. Redchuck  B.
Ricci  A. Romani  G. Sava  A. Serafini  C. Sirignano  M. Sisti  L. Stanco  E.
Stanescu Farilla  V. Strati  A. Triossi  C. Tuve  C. Venettacci  G. Verde  L.
Votano  INFN, Sezione di Milano e Università degli Studi di Milano,
Dipartimento di Fisica, Italy  INFN, Sezione di Ferrara, Italy  Università
degli Studi di Ferrara, Dipartimento di Fisica e Scienze della Terra, Italy
INFN, Sezione di Catania e Università di Catania, Dipartimento di Fisica e
Astronomia, Italy  INFN, sezione di Padova, Italy  INFN, sezione di Padova e
Università di Padova, Dipartimento di Fisica e Astronomia, Italy  INFN,
sezione di Roma Tre e Università degli Studi Roma Tre, Dipartimento di Fisica
e Matematica, Italy  INFN, Sezione di Milano Bicocca e Dipartimento di Fisica
Università di Milano Bicocca, Italy  INFN, Sezione di Perugia e Università
degli Studi di Perugia, Dipartimento di Chimica, Biologia e Biotecnologie,
Italy  Laboratori Nazionali dell’INFN di Frascati, Italy  INFN, Sezione di
Milano Bicocca e Dipartimento di Energetica, Politecnico di Milano, Italy

###### Abstract

The optical and radiochemical purification of the scintillating liquid, which
will fill the central detector of the JUNO experiment, plays a crucial role in
achieving its scientific goals. Given its gigantic mass and dimensions and an
unprecedented target value of about 3$\%$/$\sqrt{E(MeV)}$ in energy
resolution, JUNO has set severe requirements on the parameters of its
scintillator, such as attenuation length (Lat>20 m at 430 nm), transparency,
light yield, and content of radioactive contaminants (238U,232Th<10-15 g/g).
To accomplish these needs, the scintillator will be processed using several
purification methods, including distillation in partial vacuum and gas
stripping, which are performed in two large scale plants installed at the JUNO
site.

In this paper, layout, operating principles, and technical aspects which have
driven the design and construction of the distillation and gas stripping
plants are reviewed. The distillation is effective in enhancing the optical
properties and removing heavy radio-impurities (238U,232Th, 40K), while the
stripping process exploits pure water steam and high-purity nitrogen to
extract gaseous contaminants (222Rn, 39Ar, 85Kr, O2) from the scintillator.
The plant operating parameters have been tuned during the recent commissioning
phase at the JUNO site and several QA/QC measurements and tests have been
performed to evaluate the performances of the plants. Some preliminary results
on the efficiency of these purification processes will be shown.

###### keywords:

LAB , liquid scintillator , purification , distillation , stripping , JUNO

††journal: Nuclear Instruments and Methods in Physics Research A

##  1 Introduction

In the research field of neutrino physics, liquid detectors have been widely
used to investigate the properties of these weakly interacting particles.
Organic scintillators have been chosen as detection medium by several
experiments, such as Borexino [1], KamLAND [2], Daya Bay [3], RENO [4], JUNO
[5] and SNO+ [6], and the size and masses involved as detector target have
kept on growing over the years.

Due to their homogeneity, flexible handling, affordability, availability in
large quantities and possibility of different purification methods, liquid
scintillators represent an optimal solution. So far, Borexino and KamLAND
above all managed to achieve extraordinary results for radiopurity levels even
in the kiloton scale.

JUNO (Jiangmen Underground Neutrino Observatory) is a new-generation liquid-
scintillator reactor antineutrino experiment, currently under construction in
an underground laboratory (700 m vertical overburden) near Kaiping, Guandong
province, in South China. The primary goal is the determination of the
neutrino mass ordering at 3$\sigma$ level in 6 years, by detecting reactor
antineutrinos from two nuclear power plants, located approximately 53 km away.
The gigantic central detector (CD) is composed by a transparent acrylic sphere
of 35.4 m in diameter, designed to contain about 20.000 ton of liquid
scintillator (LS). 17.612 large (20”) photomultipliers and 25.600 small (3”)
photomultipliers (PMTs) installed around the acrylic vessel will detect the
scintillation light produced inside, providing a large total coverage of about
78$\%$. These features are designed to reach an unprecedented energy
resolution for a liquid scintillator detector of about
$\sim$3$\%$/$\sqrt{E(MeV)}$. The CD is immersed into a high-purity water pool,
aiming to shield the active region from the natural radioactivity of the
external environment and acting also as a muon veto detector, together with
the muon veto top tracker [7], to reject the cosmic rays background.

To accomplish the extensive scientific program of JUNO [8] [9] [10] [11] [12],
a high sensitivity and an extremely low background [13] are mandatory. For
this purpose, one of the crucial tasks is the purification of the liquid
scintillator, which is pivotal to achieve the optical and radiopurity levels
required for the scintillator. Indeed, if radioisotopes are present inside the
scintillating mixture, their signal will be detected, superimposing to the
real neutrino signal, while optical impurities could spoil its transparency
and affect the light propagation and detection, and hence the overall energy
resolution. Several chemical techniques are available to purify liquid
scintillators, but the challenging needs of neutrino physics frontier research
imply special attentions and a strong push forward in technological and
engineering solutions beyond the state-of-the-art.

In the following, the purification strategy of JUNO will be presented, paying
particular attention to the distillation and gas stripping purification
processes.

The layout of this paper manages as follows. Section 2 briefly introduce the
Juno liquid scintillator and its purification strategy. The description of the
distillation and stripping plants is shown and discussed in section 3.
Finally, plants commissioning and preliminary purification results are given
in section 4.

##  2 Juno liquid scintillator

The scintillator of JUNO is based on organic solvent, the linear alkyl benzene
(LAB), whose formula can be written as C6H5CnH2n+1, with n typically between
10 and 14. The fundamental part of the molecule is the benzene ring, which can
be excited by ionizing radiation, thus assuring the scintillating feature.
Given the low costs and possibility of huge scale mass production, high
transparency and compatibility with acrylic, good properties in terms of
safety and health hazards, the LAB represents an optimal choice as solvent. As
primary fluor, the 2,5-Diphenyloxazole (PPO) is added to it, to enhance the
scintillation, while the 1,4-Bis(2-methylstyryl)benzene (bis-MSB) has been
selected as wavelength shifter, to further reduce the self-absorption and
optimize the coupling with the PMTs in the wavelength region of their highest
sensitivity, i.e. around 430 nm.

The scintillating mixture was optimized with a dedicated test campaign at
DayaBay laboratories [14], where different concentrations of PPO and bis-MSB
were added and studied. The final JUNO LS recipe has been set to have 2,5 g/L
of PPO and 3 mg/L of bis-MSB.

The experimental goals of JUNO impose several requirements on the LS, mainly
in terms of optical and radiopurity features. Since a low background is
mandatory for JUNO to unravel rare neutrino events, the content of radio-
impurities and radioisotopes dissolved in the scintillator must be minimized,
to prevent undesired signals. Typically, the main sources of contamination,
that could be naturally present in the scintillator or introduced from the
external environment (dust, air, residual microparticles washed off and/or
emanated from surfaces contacted with liquid), are 238U, 232Th, 40K as heavy
impurities and 222Rn, 85Kr and 39Ar as gaseous impurities. The minimum
radiopurity levels in JUNO are settled for the detection of antineutrinos from
the nearby nuclear plants, consisting in 238U,232Th<10-15g/g and 40K<10-16g/g.
Instead, for solar neutrino studies, the levels must be lowered at 10-16g/g,
or even below for better precision measurements. Even at 10-16g/g, we foresee
to improve the Borexino’s measurements for 7Be neutrinos in 1 year of data
taking and for neutrinos from CNO cycle in 6 years of data taking [15], which
is the same acquisition time needed to determine the neutrino mass ordering at
3$\sigma$ level. In general, the optimal level for 238U/232Th/40K impurities
would be less than 10-17 g/g, established as final target value in JUNO. More
details about JUNO radiopurity requirements are reported in Table 1.

Concerning the optical requirements, the transparency and the attenuation
length must be carefully controlled to maintain an optimal light propagation
and thus a good energy resolution. Given the huge dimensions of the central
detector (35,4 m in diameter), the optical path of the scintillation light
before reaching the PMTs outside could be very long. Hence, the requirement on
the attenuation length of the final scintillator mixture is fixed to be Lat>20
m at 430 nm. The light yield is expected to be around 1.500 p.e./MeV, leading
to an unprecedented energy resolution of 3$\%$ at 1 MeV. The energy response
linearity and the long-term stability with ageing are other important features
to be considered.

To satisfy all the stringent requirements of JUNO LS, a dedicated procedure of
purification in several steps has been studied and implemented for the
scintillator and is described in the next sub-section.

Radioisotope | Contamination source | Typical value | JUNO minimum requirements
---|---|---|---
222Rn | Air, emanation from material | <100 Bq/m3 | <5 mBq/m3
238U | Dust suspended in liquid |  $\sim$10-6 g/g | <10-15 g/g
232Th | Dust suspended in liquid |  $\sim$10-5 g/g | <10-15 g/g
40K | Dust suspended in liquid, PPO |  $\sim$10-6 g/g | <10-16 g/g
39Ar | Air |  $\sim$1 Bq/m3 | <50 $\mu$Bq/m3
85Kr | Air |  $\sim$1 Bq/m3 | <50 $\mu$Bq/m3
Table 1: List of the main radio-contaminants that can be present in JUNO
liquid scintillator, the relative contamination sources and their typical
values. The minimum radiopurity levels for JUNO LS, required for the
antineutrino reactor program, are reported in the last column.

###  2.1 Purification strategy for Juno LS

As done in other neutrino experiments based on liquid detectors, a
purification strategy with different chemical techniques is crucial to
suppress the background deriving from radio-contaminants present in the
scintillator itself and improve its purity up to the required standard. In
JUNO, the LS is processed by a sequence of 5 purification systems:

  * 1.

filtration of raw LAB through Al2O3 (alumina) powder, in order to improve its
optical properties, increase the attenuation length and smoothen the
absorption spectrum [16];

  * 2.

distillation of LAB in partial vacuum, useful to remove heavy and high-boiling
impurities, such as 238U, 232Th and 40K, and further enhance the optical
properties;

  * 3.

acid washing of the Master Solution, created in batches with a high
concentration of PPO and bis-MSB in LAB, and then dilution until the JUNO
recipe;

  * 4.

water extraction of LS, which is effective to remove polar contaminants and
ions that may contain 238U, 232Th and 40K isotopes [17];

  * 5.

gas stripping of LS, in order to extract radioactive gases like 222Rn, 39Ar,
85Kr.

For each of these steps, a dedicated large-scale plant has been built and
installed at JUNO experimental site. After completing the first 3 processes
over ground, the LS is sent to the underground laboratory by a DN50 stainless
steel pipe through the 1,5 km slope tunnel, for the last 2 steps of the
sequence. In case of unqualified samples or if the LS already filled in the CD
needs to be re-purified along JUNO’s lifetime, it can be sent back and re-
processed underground by water extraction and stripping plants, the only two
techniques allowed after that PPO and bis-MSB have already been added to the
scintillating mixture.

##  3 Distillation and Stripping plants overview

Among the complex purification procedure for JUNO LS described in section 2.1,
this paper focuses on the details and performances of distillation and
stripping purification plants (steps 2 and 5 of the sequence, respectively),
which were entirely designed and build in Italy in collaboration with Polaris
Engineering company, Misinto (MB), Italy. The full-scale plant design was
optimized and finalized with the experience earned during the manufacturing
and commissioning of two small-scale pilot plants, installed at Daya Bay
laboratories [18].

All the plants have been realized in compliance with both Chinese and European
standards and safety rules.

###  3.1 Pilot Plants

In order to study the feasibility and the purification efficiency of these
techniques on a LAB based liquid scintillator, dedicated small-scale pilot
plants were realized and installed at Daya Bay Neutrino laboratory, near
Shenzhen, in China. For distillation and stripping processes, the research and
design phase begun in 2014, involving INFN (Istituto Nazionale di Fisica
Nucleare) and Polaris Engineering in close cooperation. Given the limited
budget, the dimensions of each pilot plant were relatively small, fitting
entirely in one 2.15 m × 2.4 m × 7 m skid, also for convenience of
transportation and handling, and with a maximum flow rate of only 100 kg/h for
the purified stream.

After the construction was completed in 2016, the pilot plants were shipped to
China by sea and installed at Daya Bay. The commissioning and intensive test
campaign were performed in 2017-2018.

The idea was to test the purification techniques and develop a deep know-how
that would have been fundamental for the optimization of the process
parameters in the full-scale plants. All the details of these pilot plants can
be found in [18].

The essential components necessary for the two processes were implemented, but
the level of plant automation was reduced to few automatic valves and
controllers, for cost-effectiveness. This aspect has been robustly upgraded in
the final plants, thus assuring a safe and automatized system able to run
stably and independently in nominal conditions and in fail-safe conditions in
case of emergencies.

For the circulation of the scintillator, the membrane dosing pumps of the
pilot plants have been substituted in the full-scale plants by magnetic driven
centrifugal pumps, more reliable and suitable for higher flow rates, and an
automatic valve to control the flux. Different types of sensors were tested,
especially to measure the level and pressure inside the distillation and
stripping columns, in the harsh conditions of partial vacuum. The number of
vacuum pumps has been increased from 1 to 4, both to satisfy the required
pumping speed and for redundancy, thus also allowing maintenance operations
without stopping the plant. Several other features have been later improved on
the basis of the knowledge acquired in this preparatory phase.

The pilot plants campaign has been successfully concluded with excellent
results about a real effectiveness in enhancing the optical and radiopurity
features of the JUNO LAB-based scintillator and laying the foundations for the
development of the full-scale purification plants, in a profitable and lasting
collaboration with Polaris company.

###  3.2 Distillation plant

The main goal of the distillation plant is the removal of the heaviest
impurities from LAB, like heavy metals and particulate that could contain
238U, 232Th and 40K radioisotopes. This process has been proven effective also
to enhance the optical quality of the solvent, mainly the absorbance and the
attenuation length in the 340-500 nm range of wavelengths.

####  3.2.1 Working principle of the distillation column

The fractional distillation is a chemical technique widely used to separate or
extract from a liquid mixture some of its components, called fractions. Taking
advantage of the different boiling points of the fractions, the mixture is
heated up until selective boiling and subsequent condensation of the desired
component occurs. The most volatile elements concentrate in higher percentage
in the evaporated phase, while the less volatile ones remain in the liquid.

In our plant, the distillation process is carried out in a 7 m-high, 2000 mm-
wide custom-made distillation column. In the bottom part, a reboiler, heat up
by hot oil, is directly connected to the column to boil the LAB and to produce
purified vapours, which are then collected and liquified at the top of the
column by a condenser.

The column is equipped with 6 sieve trays (dual flow trays) with $\sim$3500
holes (12 mm in diameter), where a layer of liquid can build-up, ensuring an
intimate contact with the upward stream of vapours. The purification process
is driven by both heat and mass transfers between the gas and liquid phases at
each tray, thus forming multiple equilibrium conditions along the height of
the column. The vapor and liquid fluxes should be limited to the design
operating range, so that a suitable contact surface and time at each stage can
be ensured.

The distillation of LAB is performed at 210-220°C in partial vacuum (60 mbar
at column bottom), in order to reduce the boiling temperature, avoiding risks
of thermal degradation and increasing the separation capability with respect
to heavy impurities.

The vacuum level inside the column is kept constant by 4 vacuum pumps (VP)
connected in the upper part, after the condenser. Pressure is the key
operating parameter that drives all the others inside the column: by setting
the pressure values at the bottom (60 mbar at the reboiler) and top (5 mbar at
the condenser) parts, the first one determines the boiling temperature of the
liquid, which is constant during phase transitions, while the latter is
adjusted by VPs through an automatic regulating valve. The pressure difference
between them corresponds to the total height of liquid accumulated on the
trays: considering 55 mbar of pressure difference and a LAB density of 720
kg/m3 at 200°C average temperature, the height is approximately 78 cm, thus
implying a liquid layer of $\sim$13 cm on each tray. So, in this design, the
liquid is held on the perforated tray by the pressure of the lower stage, in a
dynamic equilibrium between evaporation and condensation. If pressure changes
abruptly, the layers could break and the liquid would fall to the bottom of
the column. Since the layer height on the trays affects the global
purification efficiency of the distillation, the pressure values inside the
column must be carefully controlled to avoid any destabilization of it.

Among various options, sieve trays have been chosen because of their simple,
but effective layout, since they have no moving parts, can be easily cleaned,
and can be mounted inside the column without welding. A picture of them is
shown in Figure 1.

Figure 1: Installation of sieve trays inside the distillation column.

####  3.2.2 Layout of the full-scale plant

Figure 2: Flowchart scheme of the distillation plant (not in scale). The input
LAB is fed into the feed tank, heated up to 180°C by the heat recovery and the
pre-heater and inserted in the column above the 3rd tray, where it falls down
by gravity in the bottom part. Here it’s evaporated by the reboiler at about
210-220°C. The stream of purified LAB vapours is extracted and condensed in
the top part by the condenser and a certain fraction (up to 50$\%$) is sent
back to the column as an internal reflux. The distilled LAB is then cooled
down to ambient temperature by the heat recovery and the product cooler and
stored into the 20 m3 horizontal tank. The bottom of the column is discarded
regularly into the waste bottom tank to remove the contaminants. The pressure
inside the distillation column, the product tank and the bottom tank is kept
constant at 5 mbar with a set of 4 vacuum pumps (VP). Finally, after being
filtered, the distilled LAB can be either pumped out toward the Mixing plant
or sent back to the feed tank for internal loop circulation.

The plant is designed to operate at 7 m3/h of nominal flow rate. A sketch of
its flow diagram is reported in Figure 2, while the main parameters are listed
in Table 2. The LAB entering the plant is collected in a 20 m3 vertical feed
tank and pumped to the distillation column to start the purification. The
liquid is fed at mid-height, above the third tray, after being pre-heated by
two counter-flow heat exchangers at about 180°C, thus avoiding
destabilizations of the column temperature profile. In the first exchanger,
the inlet cold LAB is heated-up by the distilled hot condensate, allowing to
recover heat and save energy (in the order of 400 kWth); the second one,
supplied with hot oil, makes the final temperature adjustments of the column
feed. The liquid, falling by gravity, is collected in the bottom part of the
column vessel and boiled by the tube-bundle reboiler, thus generating LAB
vapours. High-boiling and low-volatility impurities that accumulate in the
unevaporated liquid phase remain in the bottom part and are regularly
discarded in a waste bottom tank every 30 minutes, up to 2$\%$ of the plant
nominal flow rate (7 m3/h).

Table 2: Main features and operating parameters of the distillation plant. Distillation Plant Parameter | Value
---|---
Sieve Trays | 6 trays
Tray Holes | 3500 holes (diameter 12 mm)
LAB Nominal Flow Rate | 7 m3/h
Reflux Rate | Up to 50%
Bottom Discharge | 1-2%
Pressure (at Column Top) | 5 mbar
Temperature @ Reboiler | 210-220°C
Heating Thermal Power | 1000 kWth
Heat Recovery | 400 kWth
Column Diameter | 2000 mm
Column Height | 7 m
Plant Dimensions | 10 m x 9 m x 14 m
Approx. Plant Weight | 55 tons

With the experience of the pilot plant, the number and type of sensors used to
monitor the level of liquid inside the column have been increased, for
redundancy: a safe and efficient operation requires the level to be kept
within a specific range, to avoid both flooding phenomena or lowering the
level below the reboiler. Three level switches, one differential pressure
transmitter and one guided-wave level radar are installed in the lower part of
the column, while one differential pressure transmitter measures the layers of
liquid on the trays.

Purified LAB vapours, that rise up towards the column head, are extracted and
liquified by a two-phase condenser, supplied with water at ambient temperature
from a cooling-tower. A fraction, up to 50$\%$, of the distilled liquid stream
is immediately reintroduced into the column as reflux ratio, hence further
increasing the purification efficiency. The remaining part is conveyed by
gravity to the 20 m3 product tank, after being cooled down to ambient
temperature. For technical needs and plant size optimization, the product tank
is arranged horizontally and is kept at the same pressure of the column head.
This forced us to place the circulation pump of LAB product inside a 4,5
m-deep well, dug into the ground floor, to ensure a suitable NPSH (Net
Positive Suction Head) at the pump inlet nozzle, to avoid running the pump dry
and to prevent cavitation phenomena. The pump pushes the purified liquid
through a set of 50 nm filters, with the purpose of retaining any dust or
microparticles that may have been washed off the surfaces.

Figure 3: Distillation plant installed in the Over Ground LS building at JUNO
site.

Finally, the distilled LAB can be either sent forward to the Mixing plant or
circulated back to the inlet feed tank for further purification in internal
loop mode.

The plant is equipped with 4 vacuum roots pumps, to keep a constant pressure
of 5 mbar at the column head and inside the product and the bottom tanks. The
VPs are preceded by a vent condenser, which removes any LAB droplet from the
incondensable gases sucked by the pumps. Since in the pilot plant it has been
observed that some LAB vapours could anyway reach the VP, especially during
the start-up phase, in the full-scale plant a small pre-vent condenser has
been added on the vacuum line directly near the column.

Each equipment is supplied with a continuous stream of nitrogen (N2) as
blanketing to avoid any oxidation.

The distillation plant is now installed in the Over Ground LS building at JUNO
site (Figure 3). It has been built and pre-assembled in Polaris’s workshop in
6 skids, plus one vertical and one horizontal tanks, and then shipped to China
by sea. Due to its huge dimensions, the installation has been performed from
the roof of the building with a 120-ton truck crane, implying challenging
engineering solutions.

###  3.3 Stripping plant

The stripping plant is the final stage of the purification procedure for JUNO
LS. Gas stripping process is used to remove gaseous impurities naturally
dissolved into the scintillator, mainly 222Rn, 85Kr, 39Ar radioisotopes, which
could generate undesired signals, and O2, that is responsible for photon
quenching and oxidation in the LS.

####  3.3.1 Working principle of the stripping column

It is a gas-liquid separation process where gases dissolved in a liquid phase
are extracted by desorption mechanisms exploiting a stream of pure stripping
gas. This purification technique relies on Henry’s law, which states the
dependency of the amount of a gas _i_ in a liquid being proportional to its
partial pressure (i.e. left side of equation 1):

$\centering y_{i}\cdot p_{t}=K_{H,i}\cdot x_{i}\@add@centering$ (1)

$x_{i}$ and $y_{i}$ are the molar fraction of $i$ in liquid and gas phases
respectively, $p_{t}$ the total pressure and $K_{H}$ the Henry’s constant in
atm units. The latter depends on temperature, hence changing this operating
parameter can affect the purification efficiency.

The process is based on mass transfer of the contaminant $i$ in liquid phase
passing to the vapour phase, that is the stripping gas. In an $x_{i}$-$y_{i}$
graph, the flow rates of gas ($G$) and liquid ($L$) streams determine the
process operating line, with slope $L/G$, and the so-called stripping factor
$S$:

$\centering S=\frac{L\cdot K_{H}}{G\cdot p_{t}}\@add@centering$ (2)

This quantity represents the removal rate of the contaminant between two
equilibrium stages.

Our plant is equipped with a 9 m-high custom-made vertical stripping column
filled with unstructured packing, i.e. AISI316 metallic Pall rings of 13 mm
diameter (specific interface area $a$ = 430 m2/m3; see Figure 4), with the
purpose of spreading the liquid in thin films or drops, thus increasing the
contact surface between the phases. The mass transfer directly depends upon
the interface surface exposed between gas and liquid phases: the larger this
parameter, the higher will be the purification efficiency. Ultrasonic bath
cleaning was adopted to cleanse the Pall rings before assembling and filling
the column.

Figure 4: Filling of the stripping column with unstructured packing (Pall
rings, 13 mm of diameter).

The process is performed in counter-current flow mode, feeding the LS from the
top and the gas from the bottom. An adjustable mixture of high-purity nitrogen
(HP-N2, up to 50 Nm3/h) and ultra-pure water steam (UPW, up to 30 kg/h) is
supplied as stripping gas. At top and middle height, the column features a
distributor tray, which restores a uniform spatial distribution of liquid flux
inside.

Also this plant is designed to operate in partial vacuum, at about 250-300
mbar, and up to 90°C, so that the LS viscosity is reduced and the global
efficiency is increased, as can be inferred from equation 2.

In the Daya Bay tests, the stripping process was performed at 90°C at 300 mbar
and had a purification efficiency for 222Rn of 95,8$\%$ on a 115 L/h LS flux
with a stream of 1 Nm3/h HP-N2. The dimensions of the column have been scaled
up to design the final plant for JUNO, but the height has been further
increased of 50$\%$. Based on simulations, the efficiency for the full-scale
plant is foreseen to be about 99$\%$ for a LS flow rate of 7000 L/h, with 30
Nm3/h HP-N2 as stripping gas.

####  3.3.2 Layout of the full-scale plant

The design and layout of the plant are based on a nominal LS flow rate of 7
m3/h. The flow diagram and the operating parameters are reported in Figure 5
and Table 3, respectively.

Figure 5: Flowchart scheme of the stripping plant (not in scale). The LS
entering the plant is pumped from the 20 m3 feed tank through the inlet
filters and heated by the heat recovery and the latter heat exchanger. The LS
is then inserted into the stripping column from the top, falling down by
gravity, while the stripping gas, an adjustable mixture of high-pure nitrogen
and ultrapure water steam, is fed from the bottom. The purified LS is
collected at the bottom of the column, cooled down to 21°C and stored into the
product tank. Finally, it is filtered passing through the outlet filters and
it can be pumped either to the next stage or sent back to the feed tank for
internal loop circulation. The vacuum level inside the column is kept constant
by a system composed of a vacuum condenser and 4 vacuum pumps.

The LS, delivered by the Water Extraction plant, is collected into the 20 m3
vertical feed tank and pumped through a first set of 50 nm filters, to prevent
dust from previous stages or transfer pipelines to pollute the internal
surfaces and the column packing. The scintillator is heated-up to the process
temperature, before being fed at the column head. Similarly to the
distillation plant, we take advantage of heat recovery from stripped hot
scintillator in a first heat exchanger, while a second one adjust the
temperature to the target value.

Table 3: Main features and operating parameters of the stripping plant. Stripping Plant Parameter | Value
---|---
Column filling | Unstructured (Pall-rings)
Pall-Rings diameter | 13 mm
Specific Interface Area | 430 m2/m3
LS Nominal Flow Rate | 7 m3/h
HP N2 Flow Rate | Up to 50 Nm3/h
UPW Steam Flow Rate | Up to 30 kg/h
Pressure | 250 mbar
Temperature | 70-90°C
Heating Thermal Power | 200 kWth
Heat Recovery | 160 kWth
Column Diameter | 500 mm
Column Height | 9 m (5.6 m of packing)
Plant Dimensions | 6.5 m $\times$ 9 m $\times$ 12 m
Approx. Plant Weight | 35 tons

The falling liquid is contacted and stripped by the upward stream of gas
mixture. The HP-N2 is supplied by a dedicated plant, exploiting purification
through low-temperature adsorption technology to remove radioisotopes from the
gas. This is important because their residual content sets the purification
limit that can be reached in the stripping process.

The water steam (30 kg/h maximum) is produced at 65°C at about 250 mbar by an
oil-based evaporator, starting from ultra-pure water (>18 MΩ$\cdot$cm). A
calibrated orifice of 1 cm in diameter controls the flux injected into the
column. As soon as the vapors enter the column, which is kept at higher
temperature, they become superheated, hence preventing condensation.

Figure 6: Stripping plant installed in the Underground LS Hall at JUNO site.

After being stripped, the LS is cooled down to 21°C and stored into the 20 m3
vertical product tank. The final step is the filtration through another set of
50 nm filters, that restrain any dust or metallic particulate that could have
been release by the Pall rings or other surfaces. The purified LS can be
either sent to the Central Detector of JUNO for its filling procedure or
recirculated back to the inlet feed tank for internal operation, in case of
need.

A set of 4 vacuum roots pumps is connected at the column head to keep the
pressure constant at 250 mbar and suck the exhausted stripping gas out of the
column. Condensable vapours, as water steam, are liquified and drained by a
vent condenser, while the non-condensable ones, like nitrogen, are expelled by
the pumps themselves.

The stripping plant is installed in the Underground LS Hall at JUNO site. It
is composed of 2 vertical tanks and 3 skids, with 5 walkable floors in total.
Each unit has been transported to the underground laboratory though the 1,5 km
slope tunnel and assembled using the overhead crane installed on the LS Hall
roof. A picture of the stripping plant is shown in Figure 6.

###  3.4 Common Features

Some common criteria and concepts have driven the design and the production of
these two plants.

####  3.4.1 Cleanliness and air tightness requirements

Several additional precautions have been adopted in order to achieve the
desired LS purity and avoid spoiling it.

Selected 316L stainless steel was chosen as material to manufacture all the
main components of the plants and all parts contacted with the scintillator.
In addition, a special cleaning procedure was studied and implemented to treat
the surfaces and prevent oxidation phenomena: a mirror finishing of the steel
through electropolishing below 0,8 $\mu$m; pickling and degreasing of the
welds; a mild passivation to create an inert layer, using a solution of nitric
acid (25$\%$) and demineralized water (75$\%$); rinsing with water until
conductivity and pH values were restored; compressed air-drying process with
dehumidifier and coalescing filters and final storage under N2 atmosphere.
Several quality controls were performed to certify the cleaning procedure,
such as particle counting test, endoscopic inspection, and white wipe test. A
final rinsing with high purity water was performed at JUNO site, after plant
installation and skids assembling. The rinsing water has been analysed via
particle counting to fulfil MIL STD 1246C – Class <50 cleanliness level [19].

To maintain the required level also during run phase, 50 nm pore size pre-
wetted filters have been installed on the scintillator pipelines, with the aim
of preventing circulation of dust and metallic particulate that could be
present inside the plants, despite the special cleaning procedure adopted. To
allow maintenance operations on filters without forcing a stop of the plant,
every filtering stage is composed by two independent filter holders, which
allocates six 20 inch-filter cartridges each. In the distillation plant, the
filtration is carried out as final step on the outlet line, while for the
stripping both on the inlet (before the stripping column, to avoid the dirty
being trapped into the unstructured packing) and outlet lines.

Since radioactive gases and oxygen can spoil the LS, all the plant must be
air-tight proof. The layout of the flanges has been designed with a double
o-ring protection (Viton gaskets for flange exposed to temperatures <150°C,
Kalrez gaskets for higher temperatures). Each component, flange or moving part
have been tested with a helium leak detector to check the sealing. JUNO
requirements set the thresholds of <10-8 mbar·L/s for single leak rate and
<10-6 mbar·L/s as overall integral leak rate. Everything was certified to be
within specifics. Moreover, the flanges are continuously purged by a constant
flux of HP-N2, to further reduce any possible air leakage from outside.

A procedure of vacuum pumping down to mbar level and nitrogen purging above
1,2 bar was repeated three times, to remove air before filling the plant with
the scintillator. During operation phase, all tanks and the columns are kept
under HP-N2 blanketing, in overpressure (compared to atmospheric pressure)
whenever feasible.

####  3.4.2 Mechanical aspects

All main components and pipelines have been pre-assembled and mounted inside
skids before shipping them to China. Orbital welding or TIG welding were
adopted whenever possible, while interconnections between skids or equipment
have been fitted with flanges sealed with o-rings. For ¼” sampling ports, VCR
fittings with metal gaskets were chosen.

All main pipelines for LS circulation have been equipped with full-bore ball
valves, while pneumatic regulating valves have been installed in crucial
points to remotely control the most important plant parameters, such as feed
and product fluxes and the pressure in the column.

To improve the thermal efficiency, Rockwool is adopted as insulating material
for hot pipelines and equipment, while Armaflex for the cold ones.

Each plant is provided with 4 vacuum multi-stage roots pumps, mounted in
parallel and located in the upper floors of the plant, near the main column,
to keep the design vacuum level during the purification process. The 3
magnetic driven centrifugal pumps (CDR pompe s.r.l. company) for the
circulation of the scintillator inside the plant are positioned on the ground
floor (or even beneath it, in the distillation plant) to ensure stability and
a proper NPSH.

Both plants can be operated either in internal loop mode, during self-
cleaning, commissioning and start-up operations, where the liquid is
recirculated back to the input feed tank, or in production mode, where the
processed scintillator is sent forward to the next stage of the purification
sequence.

####  3.4.3 DCS and automation

The plants are controlled by a reliable and safe Distributed Control System
(DCS), which sets the operating parameters, monitors their trend, and actuates
an alarm $\&$ interlock system in case of out-of-range values or emergency.

It is based on a Siemens PLC, model ET200S, installed in a control panel along
with the I/O modules, and it is supervised by a local pc with a dedicated
SCADA software with user interface (UI). The PLC communicates with the pc
through a MODBUS serial line, while field signals from instruments and sensors
are transmitted to control panel though 4-20 mA wiring protocol.

The PLC has three main logic areas: the main control functions, always active
to monitor the operating parameters and adjust accordingly the automatic
valves and items to reach the setpoint value; the automatic sequences, that
are set by the operator to be run independently and automatically by the
system under certain conditions; the alarms and interlocks, which set the
operating and safety ranges for each variable, provide acoustic and visual
alarms if the ranges are exceeded and inhibit some functionalities in case of
emergency, until shutdown in the worst cases. The interlocks can be software
based, the most common one, where the inhibition is activated by the SCADA
software with logic commands (but can also be bypassed in case of need), or
hard-wired based, used only for critical items, where the electric signal is
physically interrupted by opening the circuit.

The supervision system relies on a user-friendly panel with different pages,
that allows an easy and efficient monitoring of the plant. The Run page
displays a scheme of the plant, with all the field variables measured in real-
time. The second page collects a list of all alarm signals that have occurred,
both past and ongoing. In the last page, several graphical trends for the main
operating parameters are reported: they show the history of values measured
during time for each variable and they are crucial to understand the dynamic
behaviour of the plant.

####  3.4.4 Safety

The plants have been studied to fulfil several international regulations and
certifications and they have received the EC declaration of conformity.

Skid frames and structural loading diagrams have been designed to meet both
European and Chinese safety regulations. For JUNO experimental site, all
equipment must comply with the National Standard of People’s Republic of China
Code of Seismic Design of Buildings GB 50011-2001. During calculations, grade
VII seismic fortification intensity with a 0.10g horizontal acceleration was
considered.

All tanks, vessel, columns, and main components have been certified according
to PED - Group1 – Category IV – Module G classification of PED directive for
pressurized equipment. After the construction phase, they have undergone both
overpressure and vacuum tests. Rupture disks are installed on all tanks and
the two columns and are designed to break at 3,5 barg, while a pressure safety
valve is mounted on the HP-N2 feed line. The plants have also been licensed by
the Chinese authority SELO (China Special Equipment Licensing Office).

For fire protection, all electrical equipment is provided with certification
for ATEX area classification, in Class 1 Zone 2 T2. Actually, in the
distillation plant, the LAB is heat-up to temperatures above its flash point.

Security lights, active also in case of power outage, have been installed to
illuminate any access points and paths.

A detailed Hazop (HAZard and OPerability) analysis for industrial risk
assessment was carried out in collaboration with a team of international
experts. The major hazards arising from normal and abnormal operation were
identified, together with their occurrence probability and severity. Each
functional block of the plant was isolated and examined for possible
deviations from the design intention. For the hazards, preventive and
remedying actions were defined.

##  4 Commissioning and preliminary results

During an intensive commissioning phase, the distillation and stripping plants
have been run repeatedly, first in internal loop mode to test and optimize the
operating parameters, then in production mode for the joint commissioning with
all the other purification plants. They were operated for more than 8h
uninterruptedly for many times, in order to test both the stability and the
repeatability of the operating conditions. All parameters were tuned to
maintain a production flow rate of 7 m3/h, which is required to complete the
CD filling in 6 months, and all other nominal values, while providing an
efficient purification. Both demonstrated an excellent stability and safety
level throughout the whole testing period.

For the distillation plant, the condenser for purified LAB vapours after the
distillation process represents the most critical component to be set, since
the condensed liquid is split between the reflux stream back to the column and
the product one, send to the production tank, that must be kept at nominal 7
m3/h. Fine adjustments must be done to control both the flow rates and the
outlet temperature from the condenser. Subcooling below 100°C should be
avoided to prevent thermal destabilizations of the column by the reflux and
take advantage of the heat recovery exchanger exploiting the product stream.
For these reasons, the condenser’s cooling power was gradually tuned by
carefully regulating its valves, while continuously monitoring the LAB outlet
values. The optimal configuration was found to be about 185°C at the column
top, 130°C as outlet temperature from the condenser and a reflux flow rate of
4-5 m3. During the whole commissioning, almost 250 m3 have been distilled in
total, in order to tune all working parameters and gain experience in managing
the plant during all phases, from the start-up to the shutdown.

Concerning the stripping plant, several tests of the stripping purification
process were carried out using both HP-N2 and UP water steam, to check the
feasibility. With the latter, some issues emerged because of water solubility
levels into LAB changing with temperature. Actually, cooling the scintillator
back to room temperature after stripping at higher temperatures caused
dissolved water to condense, resulting in a milky emulsion of suspended water
droplets, that temporarily compromised the LS transparency. Following a
recovery period exceeding 24 hours, the LS and water separated, thereby
restoring the quality of the first. Nonetheless, it was determined safer to
exclusively use nitrogen for the stripping process, to prevent an excessive
water content in the scintillator.

After this effect was pointed out, further investigation also on the water
extraction process (refer to section 2.1) was performed through a joint
commissioning together with the stripping plant. In particular, the removal
efficiency of water and the residual water content after the stripping process
at different process temperatures and pressures were tested. The temperature
was ranged between 70°C and 90°C, while the pressure inside the column from
250 mbar to 350 mbar. The water content essay was performed before and after
the stripping process with a dedicated apparatus. The best working
configuration was determined to be 70°C at 250 mbar, using 15 Nm3/h HP-N2 as
stripping gas: with these parameters, the water content was reduced from 154
ppm down to 20 ppm, which complies with JUNO standards.

Concerning optical and radiopurity features, to carefully verify the
performances and the purification efficiencies of both plants, a comprehensive
set of several measurements with different techniques was established,
including absorption and emission spectra, particle counting, evaluation of
the attenuation length, ICP-MS and NAA. After each plant operation, samples
were taken and analysed to check compliance with the stringent JUNO
requirements. Preliminary results of some of them are presented in the
following.

Figure 7: Absorption spectra, in arbitrary units a.u., of raw LAB (grey,
dotted line), distilled LAB (green, solid line) and stripped LAB+PPO (blue,
dash-dot line).

In Figure 7, is reported a comparison between the absorption spectra, in
arbitrary units a.u., of raw LAB (grey, dotted line), distilled LAB (green,
solid line) and stripped LAB+PPO (blue, dash-dot line). The absorption peaks
at 370 nm and 390 nm of the grey curve have been crucially decreased after the
distillation process performed directly on raw LAB, thus proving also good
optical purification capabilities of this plant. Then, the solvent has been
mixed with PPO, sent to underground and purified by the stripping process,
resulting in the blue line: the insertion of PPO causes a natural shift of the
absorption edge toward longer wavelengths, around 355 nm, but all the rest of
the line is almost overlapped to the green one, meaning that this process did
not affect nor introduce major optical contamination sources into the
scintillator, also thanks to the cleaning and sealing solutions adopted.

This was confirmed also by the excellent particle counting results, which are
reported in Table 5. These values should be compared with the ones shown in
Table 4, i.e. the Class 50 level of the MIL STD 1246C [19], adopted as
standard reference in JUNO. For each particle size, the number of particles
present in the sample must not exceed the threshold value. In Table 5, the
result of the tests on samples collected right after the stripping plant. The
sensitivity of the instrument was much higher than required by the Class 50
standard; anyway, the counts for the largest particles detected (0,3 and 0,5
$\mu$m) in the samples were already 0, while for Class 50 requirement the
smallest particle size to be monitored is 5 $\mu$m and the counts should be
<1660\. This clearly demonstrates an excellent cleanliness level of all main
components and an efficient filtration system.

Table 4: The MIL STD 1246C – Class 50 cleanliness level, adopted as standard
reference for JUNO LS: the number of particles must be lower than the
threshold value for each particle size. JUNO Class 50 (from MIL 1246C)
---
Particle size ($\mu$m) | Count/L
5 | 1660
15 | 250
25 | 73
50 | 10
Table 5: The result of the particle counting test performed on LS samples
collected after the purification through stripping plant. Particle counting on
stripping plant samples
---
Particle size ($\mu$m) | Count/L
0,1 | 1200
0,15 | 500
0,2 | 100
0,3 | 0
0,5 | 0

Others QA/QC measurements are still ongoing to fully characterize the purified
samples after each step of the purification sequence, exploiting advanced
techniques and pushing their sensitivity and detection limit to values beyond
the state of the art. After a long phase of R$\&$D, they have been optimized,
refined, and validated. Their preliminary results on several purified LS
samples seem very promising in terms of LS quality and purification and they
will be published soon.

##  5 Conclusion

This paper summarises the design, main features and operation of the
distillation and stripping purification plants, which have been built and
installed at the JUNO site for the purification of the LAB-based liquid
scintillator for JUNO experiment. Thanks also to a preliminary test campaign
at Daya Bay laboratories, both these processes have been proven crucial for
the accomplishment of the optical and radiopurity JUNO requirements, which are
mandatory for its scientific program. The layout and design of the plants were
driven by precise technical needs, including low background, cleanliness
level, leak tightness and safety.

The plants have been recently commissioned and successfully tested to prepare
the 6-months filling phase of the JUNO detector. Preliminary results show very
effective purification performances and stable operating conditions of the
plants.

## Acknowledgments

This work is funded by the Istituto Nazionale di Fisica Nucleare (INFN),
Italy, through the JUNO experiment. Special thanks go to Michele Montuschi,
for his key contributions to the development of the DCS control system; Mario
Masetto, Eleonora Canesi, Gabriele Milone and the Polaris staff for their work
on the pilot and full-scale purification plants; and to the Chinese colleagues
Hu Tao, Zhou Li, Yu Boxiang, Cai Xiao, Fang Jian, Sun Lijun, Sun Xilei, Xie
Yuguang, Ling Xin, Han Hechong and Huang Jinhao from IHEP for their invaluable
help and support during the plant operation onsite.

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# Text Region Multiple Information Perception Network for Scene Text Detection

###### Abstract

Segmentation-based scene text detection algorithms can handle arbitrary shape
scene texts and have strong robustness and adaptability, so it has attracted
wide attention. Existing segmentation-based scene text detection algorithms
usually only segment the pixels in the center region of the text, while
ignoring other information of the text region, such as edge information,
distance information, etc., thus limiting the detection accuracy of the
algorithm for scene text. This paper proposes a plug-and-play module called
the Region Multiple Information Perception Module (RMIPM) to enhance the
detection performance of segmentation-based algorithms. Specifically, we
design an improved module that can perceive various types of information about
scene text regions, such as text foreground classification maps, distance
maps, direction maps, etc. Experiments on MSRA-TD500 and TotalText datasets
show that our method achieves comparable performance with current state-of-
the-art algorithms.

Index Terms—  Scene text detection, region multiple information perception,
arbitrary shape scene text

##  1 Introduction

Scene text detection is the task of detecting text in scene images, which is
one of the important contents of scene understanding and has significant
implications for artificial intelligence [1]. Due to its wide application
value in areas such as autonomous driving, image retrieval, product
recommendation, visual dialogue, scene analysis, etc., it has attracted
widespread attention [2, 3, 4, 5]. Especially with the development of
artificial intelligence[6, 7], scene text detection algorithms have made
significant progress. However, due to the complexity and diversity of natural
scenes, such as the arbitrary shapes, variable sizes, aspect ratios, and
random distribution of text positions in scenes, current scene text detection
algorithms still face many challenges [1, 8, 9].

Scene text detection algorithms based on deep learning can usually be divided
into regression-based algorithms[10, 11] and segmentation-based algorithms [3,
8] . In the early stages, deep learning-based algorithms treated scene text
detection as a task similar to general object detection and located the text
regions by regressing the bounding boxes[12, 10, 13]. For example, EAST [12]
locates text regions by regressing text boxe and using quadrilaterals with
rotation angles to represent the detected text regions. TextBoxes [10] adopts
an anchor-based detection method for text detection, while TextBoxes++ [13]
enables the detection of oriented text by designing offset values for
quadrilateral text boxes. Regression-based detection methods can adapt to
regular horizontal or vertical text detection. However, irregular texts are
more common in scenes, so segmentation-based methods for detecting scene text
have been proposed. For example, DB++ [8] segments the central region of the
text and expands it outward to obtain the complete text region. Segmentation-
based algorithms have pixel-level accuracy and can adapt to arbitrary shape
scene text, making them suitable for detecting irregular scene texts. However,
current segmentation-based algorithms usually only segment the information of
the text center region, ignoring other information of the text regions, which
limits the performance of such algorithms.

Fig. 1: The overall architecture of our RMIPN, which mainly consists of a
Backbone, a Region Multiple Information Perception Module (RMIPM), and a
Detection Head.

Based on the above analysis, to enable segmentation-based scene text detection
algorithms to perceive text region multiple information, we propose a plug-
and-play module called the Region Multiple Information Perception module
(RMIPM), which enhances the detection accuracy of scene text. Through the
designed RMIPM, various types of information about text regionscan be added
during the training process. The main contributions of this paper can be
summarized as follows: 1) We propose a plug-and-play module called Region
Multiple Information Perception Module (RMIPM), which enables scene text
detection algorithms to perceive text region multiple information during the
feature extraction process. 2) We embed the RMIPM into the baseline model to
propose the Region Multiple Information Perception Network(RMIPN), which can
perceive various regional information according to different perceptual
targets, such as center information, foreground classification map, pixel
distance map, pixel direction map, etc. 3) Extensive experiments show that the
proposed algorithm achieves competitive performance, demonstrating the
robustness and effectiveness of the proposed approach.

##  2 PROPOSED METHOD

###  2.1 Overview

We used DBNet[3] as the baseline, and the RMIPN is illustrated in Fig.1. Given
an input image $I\in\mathbb{R}^{H\times W\times 3}$ of scene text to be
detected, the backbone network extracts text visual features
$F_{b}\in\mathbb{R}^{\frac{H}{4}\times\frac{W}{4}\times C}$ at different
scales. Then, RMIPM perceives various types of information about the text
region to obtain text region features. Finally, the detection head completes
text detection based on the text region features.

###  2.2 Detailed Architecture

Backbone: U-Net has been widely used in visual tasks. In order to make a fair
comparison with previous algorithms, we use the U-Net with a pyramid structure
backbone similar to DB++ [8] as the model backbone. Specifically, given the
input image $I\in\mathbb{R}^{H\times W\times 3}$ to be detected, Resnet
extracts visual features as follows:

$F_{b}=U\left(I\right)\in\mathbb{R}^{\frac{H}{4}\times\frac{W}{4}\times C}$
(1)

where $H$ and $W$ represent the height and width of the original image, $C$
represents the feature dimension, and $U$ represents U-Net with ASF module[8].
It is also possible to replace U-Net as a backbone with another network, such
as a feature pyramid [14].

RMIPM: Existing segmentation-based scene text detection algorithms usually
only perform pixel-level segmentation on the text center region, while
ignoring other text region information. In order to enable segmentation-based
algorithms to perceive multiple information about text regions, we have
proposed a plug-and-play module called Region Multiple Information Perception
Module (RMIPM). The RMIPM is composed of multiple sub-modules called Text
Region Information Perception Modules (IPM).

In this paper, we designed a text center map, a foreground classification map,
a distance map from the pixels in the text area to the text edges, and a
direction map of the pixels in the text region pointing to the text edges. The
process of text region information perception by the IPM can be formulated as
follows:

$\left\\{\begin{array}[]{l}F_{C}=cat(F_{cen}^{\prime},F_{for}^{\prime},F_{dis}^{\prime},F_{dir}^{\prime}),\\\
F_{T}=R(Conv_{3\times
3}(F_{C})\in\mathbb{R}^{\frac{H}{4}\times\frac{W}{4}\times
C}\end{array}\right.$ (2)

where $F_{cen}^{\prime}$, $F_{for}^{\prime}$, $F_{dis}^{\prime}$, and
$F_{dir}^{\prime}$ represent the perceived center region information,
foreground classification information, distance information from the pixels in
the text area to the text edges, and direction information of the pixels in
the text regions pointing to the text edges. $Conv_{3\times 3}$ represents a
convolution layer with a $3\times 3$ kernel, $R$ represents the $ReLU$
activation functions.

Fig. 2: The overall architecture of Text Region Information Perception Module
(IPM). Red arrows indicate that they are only present during the training
phase

IPM: In order to perceive various types of information in the text region, we
design the text region information perception module(IPM). The structure of
the text region information perception module is shown in Fig.2. The feature
of text region information perception can be formulated as follows:

$\left\\{\begin{array}[]{l}W_{a}=S(Con{vT}_{3\times 3}(R(Conv_{5\times
5}(R(Conv_{3\times 3}(F_{b})))))),\\\ F_{e}=W_{a}\times
F_{b}\in\mathbb{R}^{\frac{H}{4}\times\frac{W}{4}\times C},\\\
F^{\prime}=F_{e}+F_{b}\end{array}\right.$ (3)

where $Conv_{5\times 5}$ represents a convolution layer with a $5\times 5$
kernel, $Con{vT}_{3\times 3}$ represents a deconvolution layer with a $3\times
3$ kernel, $S$ represents the $Sigmoid$ functions.

In the process of supervision training, the text region information perception
goal can be formulated as:

$D_{Tar}=\ Con{vT}_{3\times 3}(R(Con{vT}_{3\times
3}(F_{e}))))\in\mathbb{R}^{H\times W\times n}$ (4)

where $n$ is the channel of the feature corresponding to the perceived text
region. For example, in this paper, four types of targets are set. When the
perceived region information is a direction map, $n$ takes the value of 2
(Including horizontal and vertical directions). Otherwise, $n$ takes the value
of 1.

Detection Head: After obtaining the perceived text region features $F_{T}$,
the detection head completes the text detection. We also used a commonly used
convolution structure [8], and the detection head can be formulated as
follows:

$D_{T}=S(Con{vT}_{2\times 2}(R(Con{vT}_{2\times 2}(F_{T})))$ (5)

where $Con{vT}_{2\times 2}$ represents a deconvolution layer with a $2\times
2$ kernel and batch normalization.

(a) Sample Image

(d) Distance Map

(b) Text Center Map

(e) X Direction Map

(c) Text foreground Map

(f) Y Direction Map

Fig. 3: Examples of sample ground-truth labels for the total-text dataset. ’X
Direction Map’ represents the visualization in the x-direction and ’Y
Direction Map’ represents the visualization in the y-axis direction.

###  2.3 Training Objective Loss

Label generation: We designed four types of text region information, including
text center maps, foreground classification maps, edge maps, and pixel
direction maps. Correspondingly, four types of segmentation labels need to be
generated.

To avoid incorrectly segmenting adjacent text regions into a connected domain,
we perform segmentation detection on the central region of the text. The
center region is obtained by using the Vatti algorithm [15] to clip it from
the text foreground region. The foreground classification label of a text
region contains the entire text region, with pixel labels inside the region as
1 and outside the region as 0. The distance map is the distance from each
pixel inside the text region to the nearest text edge point, and the direction
map is the direction from each pixel inside the text region to the nearest
edge point. The visual results after normalization of the four types of labels
are shown in Fig. 3. It should be noted that the direction is divided into
x-axis direction and y-axis direction, so its feature map is a two-channel
tensor.

Multitask loss function: To perceive multiple information about text regions,
we designed a multi-objective training function. The objective function is
defined as follows:

$Loss=\alpha_{1}L_{cen}\times{\alpha_{2}L}_{for}+{\alpha_{3}L}_{dis}+{\alpha_{4}L}_{dir}+{\alpha_{5}L}_{b}$
(6)

where $L_{cen}$, $L_{for}$, $L_{dis}$ and $L_{dir}$, represent the cross-
entropy loss functions between the corresponding predicted probability map
$D_{Tar}$ and ground truth labels.$L_{b}$ is Differentiable Binarization
loss[3]. $\alpha_{1}$,$\alpha_{2}$, $\alpha_{3}$, $\alpha_{4}$, $\alpha_{5}$,
and $\alpha_{6}$ are balancing factors, all set to 1 in the experiment.

Table 1: Text detection performances comparison with other methods on TotalText. Bold indicates the highest indicator. Methods | Paper | R(%) | P(%) | F(%)
---|---|---|---|---
TextSnake[16] | ECCV’18 | 74.5 | 82.7 | 78.4
PSENET [17] | CVPR’19 | 75.2 | 84.5 | 79.6
CRAFT [18] | CVPR’19 | 79.9 | 87.6 | 83.6
DRRG[19] | CVPR’20 | 84.9 | 86.5 | 85.7
DB [3] | AAAI’20 | 82.5 | 87.1 | 84.7
FCENet [20] | CVPR’21 | 82.7 | 85.1 | 83.9
PCR [21] | CVPR’21 | 82.0 | 88.5 | 85.2
MS-ROCANet [1] | ICASSP’22 | 83.3 | 85.6 | 84.5
DB++ [8] | TPAMI’23 | 83.2 | 88.9 | 86.0
TCM-DBNet [9] | CVPR’23 | - | - | 85.9
RMIPN | Ours | 83.0 | 89.4 | 86.1

##  3 Experiments

In order to verify the effectiveness of our proposed algorithm, we conducted
experiments on publicly available benchmark datasets. This section will
introduce the experimental details.

###  3.1 Datasets

SynthText dataset [22] contains 800K samples of text rendered on scene images.
The synthetic dataset is only used for training. TotalText [23] is divided
into a training set of 1255 images and a testing set of 300 images. The scene
text in this dataset has features such as arbitrary shape and varying
orientation. The text in the scene has variable numbers of edge point
annotations. MSRA-TD500 [24] consists of a training subset of 300 images and a
testing subset of 200 images. The dataset contains multiple languages,
including English and Chinese. Text instances are annotated with text lines.
Consistent with previous research [3], we extracted 400 images from HUST-TR400
[25] for training.

###  3.2 Implementation Details

In fact, it is difficult to make an absolutely fair comparison between
different algorithms due to the differences in backbones and other training
strategies. In order to make the comparison as fair as possible, we adopted
similar or the same training strategies and settings with previous algorithms
[3, 8]. The algorithm code is implemented in PyTorch and trained on a single
NVIDIA TITAN RTX graphics card with 24G. The model was initialized using the
DB++ [8] provided by their paper. During training, the same data enhancement
techniques are used as in DB [3], DB++[8], including random rotation,
cropping, flipping, etc. During the training process, pre-training is first
conducted on the SynthText for 50k iterations, followed by fine-tuning the
corresponding real dataset for 1000 epochs. We used the SGD optimizer with a
batch size of 2. The learning rate is initialized to 0.001, the weight decay
is set to 0.0001, and the momentum is set to 0.9.

###  3.3 Comparison with SOTA approaches

We compared our proposed algorithm with the state-of-the-art algorithms, and
the statistics are shown in Tab.1, Tab 2. It can be seen that our proposed
algorithm achieved comparable performance with SOTA algorithms. Specifically,
our algorithm outperformed SOTA algorithms on the MSRA-TD500 dataset. The
visualized detection results of our proposed algorithm are shown in Fig.4.

Table 2: Text detection performances comparison with other methods on MSRA-TD500. Bold indicates the highest indicator. Methods | Paper | R(%) | P(%) | F(%)
---|---|---|---|---
SAE [26] | CVPR’19 | 81.7 | 84.2 | 82.9
CRAFT [18] | CVPR’19 | 78.2 | 88.2 | 82.9
DRRG[19] | CVPR’20 | 82.3 | 88.1 | 85.1
DB [3] | AAAI’20 | 79.2 | 91.5 | 84.8
MOST [27] | CVPR’21 | 82.7 | 90.4 | 86.4
PCR [21] | CVPR’21 | 83.5 | 90.8 | 87.0
FC2RN [28] | ICASSP’21 | 81.8 | 90.3 | 85.8
DB++ [8] | TPAMI’23 | 83.3 | 91.5 | 87.2
TCM-FCENet [9] | CVPR’23 | - | - | 86.9
MIPN | Ours | 83.4 | 92.1 | 87.5

###  3.4 Ablation Study

To validate the effectiveness of RMIPM as a plug-and-play module, we set up an
ablation study on the MSRA-TD500. The statistical results are shown in Tab 3.
By inserting the RMIPM model into the DB algorithm, recall rate, accuracy, and
F-value can be improved to some extent. This proves the effectiveness of
RMIPM.

(a) MSRA-TD500

(c) Total-Text

Fig. 4: Example of RMIPN detection results on datasets MSRA-TD500, ICDAR2015, and TotalText.(a) MSRA-TD500 dataset, (b) TotalText data set Table 3: Ablation study of RMIPM on the two datasets. DB+RMIPM indicates that RMIPM is embedded into the DB. Methods | MSRA-TD500 | Total-Text
---|---|---
R(%) | P(%) | F(%) | R(%) | P(%) | F(%)
DB [3] | 79.2 | 91.5 | 84.9 | 82.5 | 87.1 | 84.7
DB+RMIPM | 80.8 | 91.8 | 86.0 | 82.6 | 88.1 | 84.8

###  3.5 CONCLUSION

To improve the detection accuracy of existing segmentation-based scene text
detection algorithms by perceiving multiple text region information, we
proposed a Region Multiple Information Perception Network(RMIPN). The network
consists of three parts: a backbone, a RMIPM, and a detection head. RMIPM is
composed of multiple IPMs, each of which can perceive the individual
information of text regions based on perceived targets. In the paper, a text
center region information, a text foreground classification map, a text pixel
distance map, and a text pixel direction map are designed as four types of
perceivable text region information. Experiments on public benchmarks have
shown that our proposed algorithm can improve text detection performance and
is effective. Therefore, the work of this paper provides an idea for the
development of segmentation-based scene text detection algorithms. In the
future, we will explore building detection algorithms with knowledge reasoning
capabilities between scene texts to promote the development of scene text
understanding tasks.

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