PAPERmaking! Vol5 Nr1 2019

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Produced by: The Paper Industry Technical Association

Volume 5 / Number 1 / 2019

PAPERmaking! FROM THE PUBLISHERS OF PAPER TECHNOLOGY Volume 5, Number 1, 2019 CONTENTS:

FEATURE ARTICLES: 1. Wastewater : Modelling control of an anaerobic reactor 2. Biobleaching : Enzyme bleaching of wood pulp 3. Novel Coatings : Using solutions of cellulose for coating purposes 4. Warehouse Design : Optimising design by using Augmented Reality technology 5. Analysis : Flow cytometry for analysis of polyelectrolyte complexes 6. Wood Panel : Explosion severity caused by wood dust 7. Agriwaste : Soda-AQ pulping of agriwaste in Sudan 8. New Ideas : 5 tips to help nurture new ideas 9. Driving : Driving in wet weather - problems caused by Spring showers 10. Women and Leadership : Importance of mentoring and sponsoring to leaders

11. Networking : 8 networking skills required by professionals 12. Time Management : 101 tips to boost everyday productivity 13. Report Writing : An introduction to report writing skills

SUPPLIERS NEWS SECTION: Products & Services :

Section 1 – PITA Corporate Members: ABB / ARCHROMA / JARSHIRE / VALMET

Section 2 – Other Suppliers Materials Handling / Safety / Testing & Analysis / Miscellaneous

DATA COMPILATION: Installations : Overview of equipment orders and installations since November 2018 Research Articles : Recent peer-reviewed articles from the technical paper press Technical Abstracts : Recent peer-reviewed articles from the general scientific press Events : Information on forthcoming national and international events and courses

The Paper Industry Technical Association (PITA) is an independent organisation which operates for the general benefit of its members – both individual and corporate – dedicated to promoting and improving the technical and scientific knowledge of those working in the UK pulp and paper industry. Formed in 1960, it serves the Industry, both manufacturers and suppliers, by providing a forum for members to meet and network; it organises visits, conferences and training seminars that cover all aspects of papermaking science. It also publishes the prestigious journal Paper Technology International and the PITA Annual Review , both sent free to members, and a range of other technical publications which include conference proceedings and the acclaimed Essential Guide to Aqueous Coating .

Contents

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PAPERmaking! FROM THE PUBLISHERS OF PAPER TECHNOLOGY Volume 5, Number 1, 2019

Dynamic soft sensing of organic pollutants in effluent from UMIC anaerobic reactor for industrial papermaking wastewater Yajuan Xing 1 , Zhong Cheng 2 ,*, Shengdao Shan 3 . With the rapid development of paper industry, the pressure of environmental pollution is going more and more serious. Recently, resource utilization of wastewater by anaerobic digestion has become a feasible way to solve this problem. In order to maintain the safe and efficient production of the process, a novel adaptive soft sensor model was developed to infer the chemical oxygen demand (COD) of paper mill effluent in this paper. First, the principal component analysis technique was performed in this model so as to eliminate the col-linearity between the process variables and accordingly obtain the low-dimensional feature principal component. Then, the least square support vector machine method was used to construct a quantitative regression model between principal component and the effluent COD. Along with it, particle swarm optimization was implemented to search for the best value of the LSSVM model parameters, namely the kernel parameters and the regularization factor. Finally, an online calibration strategy was designed to adapt to the process dynamic changes in an adaptive iterative manner. When the constructed model tested for performances in a full-scale factory, the average relative deviation and maximum deviation are 1.80% and 6.26%, respectively. The experimental results show that this proposed soft sensor model is featured with high accuracy and strong dynamic stability, and it can provide good guidance for COD prediction and optimal control of paper mill wastewater treatment. Contact information: 1 Key Laboratory of Recycling and Eco-treatment of Waste Biomass of Zhejiang Province, School of Environment and Resources, Zhejiang University of Science and Technology, Hangzhou 310023 2 School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023 3 Key Laboratory of Recycling and Eco-treatment of Waste Biomass of Zhejiang Province, School of Environment and Resources, Zhejiang University of Science and Technology, Hangzhou 310023 Yajuan Xing et al 2019 IOP Conf. Ser.: Mater. Sci. Eng. 490 062027. https://doi.org/10.1016/j.bcab.2019.01.019

Article 1 – Wastewater

Page 1 of 11

SAMSE 2018 IOP Conf. Series: Materials Science and Engineering 490 (2019) 062027

IOP Publishing doi:10.1088/1757-899X/490/6/062027

Dynamic soft sensing of organic pollutants in effluent from UMIC anaerobic reactor for industrial papermaking wastewater Yajuan Xing 1 , Zhong Cheng 2,* , Shengdao Shan 3 1 Key Laboratory of Recycling and Eco-treatment of Waste Biomass of Zhejiang Province, School of Environment and Resources, Zhejiang University of Science and Technology, Hangzhou 310023 2 School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023 3 Key Laboratory of Recycling and Eco-treatment of Waste Biomass of Zhejiang Province, School of Environment and Resources, Zhejiang University of Science and Technology, Hangzhou 310023 *Corresponding author e-mail: chengzhong@zust.edu.cn Abstract. With the rapid development of paper industry, the pressure of environmental pollution is going more and more serious. Recently, resource utilization of wastewater by anaerobic digestion has become a feasible way to solve this problem. In order to maintain the safe and efficient production of the process, a novel adaptive soft sensor model was developed to infer the chemical oxygen demand (COD) of paper mill effluent in this paper. First, the principal component analysis technique was performed in this model so as to eliminate the col-linearity between the process variables and accordingly obtain the low-dimensional feature principal component. Then, the least square support vector machine method was used to construct a quantitative regression model between principal component and the effluent COD. Along with it, particle swarm optimization was implemented to search for the best value of the LSSVM model parameters, namely the kernel parameters and the regularization factor. Finally, an online calibration strategy was designed to adapt to the process dynamic changes in an adaptive iterative manner. When the constructed model tested for performances in a full-scale factory, the average relative deviation and maximum deviation are 1.80% and 6.26%, respectively. The experimental results show that this proposed soft sensor model is featured with high accuracy and strong dynamic stability, and it can provide good guidance for COD prediction and optimal control of paper mill wastewate treatment.

1. Introduction The paper-making industry is a major water consumer and also a major wastewater discharger. According to the statistics of the Ministry of Ecology and Environment, In 2015, the total water consumption of the paper-making industry and the paper product industry (4,180 enterprises involved in the statistics) was 11.835 billion tons, and the wastewater discharge was 2.367 billion tons,

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SAMSE 2018 IOP Conf. Series: Materials Science and Engineering 490 (2019) 062027

IOP Publishing doi:10.1088/1757-899X/490/6/062027

accounting for 13.0% of the total industrial wastewater discharge. The chemical oxygen demand (COD) in the discharged wastewater is 335,000 tons, accounting for 13.1% of the total industrial COD emission. In recent years, with the increasing shortage of water resources, production water has become a problem that restricts the development of paper-making enterprises. At present, in order to solve the environmental pollution due to paper-making wastewater and realize resource utilization, biogas production through anaerobic digestion has become a main method. The anaerobic digestion process under the action of microorganisms is featured as multi-factor influence, dynamic variability, complex nonlinearity (Yang Hao et al., 2016), etc. and the mechanism model thereof is difficult to construct, so the real-time operation control and optimization and calibration that affect safe production and effluent water production conditions cannot be realized. The production effectiveness of the industrialization process of anaerobic digestion for paper-making wastewater is often measured by the effluent COD. However, the current COD testing of enterprises is mostly realized by timed manual sampling and laboratory analysis. The test results cannot be obtained till several hours later, so the real-time performance is poor (Xu Lisha et al., 2012). In case that a COD on-line analyzer is installed on site, failure often occurs, resulting in loss of data. And also, the maintenance is difficult and the instrument is expensive(Langergraber et al., 2004; Bourgeois et al., 2010). With the improvement of enterprise automation as well as the deep integration of informationization and industrialization, the methods like pivot element regression, partial least squares regression, neural network, support vector machine and fuzzy logic have been used for the data modeling and operational control of the performance indicators including COD concentration, volatile fatty acid (VFA), dissolved oxygen, suspended solids (SS) concentration and gas production in the process of paper-making wastewater treatment (Bourgeois et al., 2010; Haimi et al., 2013) Choi et al., 2001; Wan et al., 2011; Huang et al., 2015 Dürenmatt et al., 2012; Zhou Hongbiao et al., 2017; Liu Lin et al., 2017; Tang Wei et al., 2017). With respect of the method selection, Wan et al. (2011) designed an adaptive fuzzy inference system integrating fuzzy subtractive clustering and PCA technologies, of which the fuzzy subtractive clustering is used to identify the model structure, and PCA is used to reduce the complex collinearity between variables as well as the dimensionality. The model accuracy with this integrated method is higher than that with the BP neural network method in the performance test about the COD and SS concentration prediction of paper-making wastewater. Wang Yao et al. (2017) chose the LSSVM method to predict the COD and SS concentrations. The results show that the soft-sensor model created by optimizing the LSSVM method parameters via the PSO algorithm has a higher prediction accuracy. The LSSVM method based on minimum structural risk is widely used in soft-sensor modeling because of its features of low dependence on sample data, less parameters to be estimated, and strong generalization ability (Souza et al., 2016; Wang et al., 2015; Fortuna et al., 2007; Liu Bo et al., 2015; Zheng Rongjian et al., 2017). However, the prediction accuracy of the soft-sensor model based on the offline sample data architecture, will gradually decline in the face of dynamic changes in continuous production processes. In order to solve the above problem, this paper proposes an OCS-PCA-PSO-LSSVM soft-sensor method integrating data analysis technology and regression modeling, which can eliminate the complex collinearity between variables and achieve dimensionality reduction via PCA technology; then, implement the LSSVM method to establish the nonlinear relationship between input and output variables, and realize the optimization of LSSVM model parameters by means of PSO; and finally, initiate the online calibration strategy (OCS) in case the prediction deviation of the new sample individual exceeds the set error limit, iteratively updating the soft-sensor model in an adaptive manner. 2. Materials and Methods 2.1. Process and Data Collection With the wastewater anaerobic treatment system of a papermaking mill as the test object of application, the production process is shown in Fig. 1, in which the ascending multistage internal circulation anaerobic reactor UMIC is the main device. The UMIC reactor works based on the principle of granular sludge (Ruggeri et al., 2015; Zhang Yi et al., 2014), namely, the papermaking wastewater is thoroughly mixed with anaerobic microbial sludge after being pumped into the reactor by a lift pump,

SAMSE 2018 IOP Conf. Series: Materials Science and Engineering 490 (2019) 062027

IOP Publishing doi:10.1088/1757-899X/490/6/062027

and the organics in the mixture are chemically converted into the gases like methane and carbon dioxide, as well as microbial bacterial plastids under the action of the microorganisms. Based on the analysis of production behavior and process mechanism of the UMIC device, with the combination of experts’ experience and knowledge as well as the sensitivity analysis of field data, 8 process variables that affect the COD of the treatment system were selected as the input variables of the model, and they are: influent COD/mg•L -1 , influent SS/mg•L -1 , influent pH, influent flow/m 3 , influent temperature/°C, circulating pool level/%, effluent pH and effluent temperature/°C, while the output variable of the model is effluent COD/mg•L -1 . Two sample data collection methods were adopted, one of which was that the mill’s distributed control system DCS was used to collect 8 process variables, and the other was that the on-site sampling laboratory obtained effluent COD through offline test (Sun Jun et al., 2017). After the collection of the mill’s field operation data from July 2016 to February 2017 was completed, the missing data was directly removed, then the abnormal data was identified and deleted, and finally the initial sample matrix set containing 175 sample individuals was obtained.

1-Bar screener; 2-Blending pond; 3- Preliminary clarifier; 4-Pump; 5- Regulating container; 6-Anaerobic reactor; 7-Cycling wstandpipe; 8- Anoxic pool; 9- The secondary clarifier; 10-Biogas storage tank; 11-Nutrient auto-count pipette Fig 1. Flow chart of anaerobic degradation for paper mill wastewater 2.2. OCS-PCA-PSO-LSSVM Soft-sensor Method 2.2.1. PCA Technology The independent variable matrix n p u X of the obtained initial sample data was recorded, where n is the number of sample individuals, i.e. the sample size, and p is the number of process variables. PCA technology (Jolliffe et al., 2002) was namely to project X from the Euclidean space to the latent vector space of the pivot element. T T ¦ d k k k=1 X=TQ +E= tq +E (1) Where, k t is the k th extracted pivot element, k q is the load vector used to extract the pivot element, and E is the final residual matrix. In essence, the construction of the PCA latent vector space is to represent most of the dynamic information in the initial process variables in the sample data by extracting d pivot elements ( d p d ) (Sun Jun et al., 2017), of which, the information contribution of the k th pivot element can be calculated according to Formula (2).

(2)

1 k K O O ¦ / k k k

2.2.2. Soft-sensor Optimization Model PSO-LSSVM The least square support vector machine (LSSVM) is an extension of the standard SVM method (Cristianini et al., 2000), and the main ideas of the algorithm are summarized as follows:

SAMSE 2018 IOP Conf. Series: Materials Science and Engineering 490 (2019) 062027

IOP Publishing doi:10.1088/1757-899X/490/6/062027

1 {( , )} n i i

i y t , where,

d i R t is the input vector of

Suppose the modeling sample data set is

the i th -dimension pivot element in the latent vector space expanded by the d th -dimension pivot element, and i y R is the target output variable of effluent COD of the papermaking wastewater. In the high-dimension feature space constructed by the nonlinear mapping function ( ) M t , the model establishment between the output variable and the input variable is to find the best fitting function: T ( ) ( ) y b M t w t (3) Where, w is the weight coefficient vector to be estimated in the high-dimension feature space, b is the constant deviation term. For the LSSVM method, the parameter estimate in the above formula can be transformed to satisfy the constraint of Formula (4): T ( ) , 1,2, , i i i y b i n M [ w t (4) The minimization optimization problem was solved as below: T 2 , , 1 1 1 min ( , , ) 2 2 n i b i J b [ J [ ¦ w ȟ w w w (5) In the formula, J is a penalty factor, used to balance the complexity and approximation accuracy of the model, i [ is the training error of the i th sample point. The Lagrange multiplier i D is now introduced to transform the above-mentioned constraint optimization problem of the formula into an unconstrained optimization problem:

( , , , ) ( , , ) J b w ȟ w ȟ Į

L b

(6)

( D M T

( ) w b t

)

i [

Using the KKT optimization condition to solve the above formula (Zhou Xinran, 2012), that is, to solve the partial derivatives of w , b ˈ i [ and i D , we can obtain:

0 w ° o ° w °w ° o °w ® ° w o °w ° ° w 0 0 i [ L L b L L w

( ) t

i D

(7)

, 1,2, , i

D J[

T w t ( ) o b I

0, 1,2, ,

y i

i [

° w ¯

i D

Eliminating the elements from the above equation set, we will obtain the following linear equation set: T 1 0 0 1 1 v v b J ª º ª º ª º « » « » « » ¬ ¼ ¬ ¼ ¬ ¼ I Į y K (8) Where, T 1 [1,1, ,1 ] v n , T 1 2 [ , , , ] n D D D Į , T 1 2 [ , , , ] n y y y y , T ( , ) ( ) ( ) ij i j i j K M M t t t t ˈ 1,2, , i j n ˈ ˈ and I is the unit matrix. After solving the parameters of i D and b in Formula (8) and via the least square method, the LSSVM model will be obtained as below:

( ) K b D ¦ t t t ( , ) i i

ˆ y f

(9)

2 2

If the LSSVM model uses the RBF kernel function , the different values of the kernel function width V and the penalty factor J in Formula (5) will affect the actual performance of the LSSVM model (Zhao et al., 2000). To this end, this paper completes the optimization of the two parameters by taking the minimum of the sum of squared error ( , , ) exp( V t t ) i i K V t t

SAMSE 2018 IOP Conf. Series: Materials Science and Engineering 490 (2019) 062027

IOP Publishing doi:10.1088/1757-899X/490/6/062027

v n

2 l y y ¦ between the experimental value l y and the predicted value of the model of the effluent COD as the objective function, through the particle swarm optimization (PSO) (Kennedy et al., 1995), based on the validation sample set. 2.2.3.Model Parameter Adaptive Correction 1 ˆ ( ) l l In order to track the dynamic changes of the production process and maintain the prediction performance of the soft-sensor model in real time, an online calibration strategy (OCS) has been designed to iteratively update the soft-sensor model parameters in an adaptive manner. The basic idea of OCS is that if the established soft-sensor model is applied to the prediction of COD for a new sample individual, when the deviation between the experimental value new y of the new sample individual and the predicted value ˆ new y of the model exceeds the set error limit maxe , namely: ˆ | | new new y y maxe ! (10) To Initiate the iterative update of the soft-sensor model parameters. The specific method is as follows: firstly, the sample individuals with the largest fitting deviation are retrieved from the training sample set and deleted; then, the sample individuals with the highest ranking in the monitoring sample set are transferred into the training sample set; next, the vacancy of the validation sample set is filled, namely, the sampled individuals with the highest ranking among the accumulated predicted sample individuals are transferred into the validation sample set; and finally, the soft-sensor model is re-established based on the newly formed training sample set and the validation sample set, which is namely the OCS - PCA-PSO-LSSVM model.

new X

new y

new T

ˆ ! new new maxe y y

{ , } T y

{ , } V J

{ , } X y

ˆ y

ˆ new y

Fig 2. Structure of the OCS-PCA-PSO-LSSVM soft sensor model With this, the implementation flow of the OCS-PCA-PSO-LSSVM method is shown in Fig. 2. Firstly, the PCA pre-processing of T 1 2 [ ] n p p u X = x ,x , ,x was performed and the pivot element matrix T 1 2 [ , , , ] n d d u T t t t was obtained after the number d of pivot elements had been selected; then, based on n d u T and the output variable matrix T 1 1 2 [ , , , ] n n y y y u y of the effluent COD, the nonlinear mapping relationship between them was established with the LSSVM method, while the values of

SAMSE 2018 IOP Conf. Series: Materials Science and Engineering 490 (2019) 062027

IOP Publishing doi:10.1088/1757-899X/490/6/062027

model parameters V and J were determined by PSO optimization; and finally, the OCS would be initiated to iteratively update the model in case the prediction deviation of the new sample individual was beyond the set error limit. 3.Results and Discussion 3.1. Model Performance Evaluation Indicator To objectively and independently evaluate the performance of the OCS-PCA-PSO-LSSVM soft-sensor model, the initial sample data set was divided into a training sample set, a validation set, and a test set in time order, of which the training sample set contained 100 sample individuals, used for parameter estimation of the model; the validation sample set contained 50 sample individuals, used for parameter optimization of the model; and the test sample set contained the remaining 25 sample individuals, used for performance evaluation of the model. The performance evaluation indicators include: maximum deviation (MAXE)/mg•L -1 , maximum relative deviation (MAXRE)/%, mean absolute deviation (MAE)/mg•L -1 , mean relative deviation (MRE)/% ,root mean square error (RMSE) / mg • L -1 , standard deviation (STD) / mg • L -1 , etc., and their respective definition formula are as follows:

ˆ y y

(11)

MAXE max {

}

{1,2, , } n

ˆ y y y i

(12)

MAXRE max {

} 100% u

{1,2, , } n

1 n

i ¦

ˆ y y

MAE

(13)

1 n

y y

i y

(14)

MRE

100%

1 n

( ¦ i

ˆ y y

RMSE

)

(15)

1 1 ¦ i

STD

(

)

e e

(16)

1 n ¦ 1 n

Where, i y ˆ denote the experimental value and predicted value of the model regarding COD of the i th sample individual, respectively. Among the above statistical performance indicators, MAXE, MRE, RMSE and STD are absolute accuracy indicators, of which, MAXE measures the limit boundary conditions of the model according to the maximum predicted deviation of the sample individuals, and MRE and RMSE measure the accuracy of the model according to the average prediction accuracy of the sample individuals. while STD measures the stability of the model according to the degree of dispersion of the prediction deviation of the sample individuals. Considering the objective difference between the magnitudes of different physical quantities, MAXRE and MRE are relative accuracy indicators. The former measures the deviation of the prediction results based on a single sample individual, and the latter does the same based on the average of sample individuals. The smaller the values of these statistics are, the better the performance of the model will be indicated. 3.2. Experimental Results and Analysis ˆ i i e y y , i i i e e , while i y and As described in Section 2.2.1 above, in order to satisfy that the pivot element under the OCS-PCA-PSO-LSSVM method contain enough initial variable information, and the cumulative information contribution rate of the d th extracted pivot element is now required to be above 85%. Based on the information of the eight latent roots of the correlation matrix for the training sample set

SAMSE 2018 IOP Conf. Series: Materials Science and Engineering 490 (2019) 062027

IOP Publishing doi:10.1088/1757-899X/490/6/062027

of papermaking wastewater, when the 6t h pivot element is extracted during calculation, namely, 6 d , the cumulative information contribution rate is 92.63%. Thus, the six pivot element are determined as the input vectors of the subsequent PSO-LSSVM model. As described in Section 2.2.2 above, the optimization process for the algorithm of parameters J and V PSO under the LSSVM method is shown in Figs. 3 and 4 after the RBF radial basis kernel function was selected, and the population particle number was set at 30, the minimum inertia weight was min w =0.01, the maximum inertia weight was min w =0.99, the particle maximum velocity was 2 max v , the particle minimum velocity was -2 min v , and the learning factor was 2 2 1 c c and the maximum number of iterations was 100. When J =0.3356 and V =2.2026, the RMSE of the objective function observation sample set reached the minimum, thereby it was determined as the optimal value of the parameter under the LSSVM method.

Fig 3. Regularization factor optimizing curve using PSO

Fig 4. Kernel parameter optimizing curve using PSO After the optimization for the input variable and parameter of the model was completed, the OCS-PCA-PSO-LSSVM model was applied to the test sample set to detect the model’s generalization ability. Table 1 shows the test results of different performance indicators for the three models of OCS-PCA-PSO-LSSVM, PCA-PSO-LSSVM and SVM. It may be observed from the table that the values of the maximum deviation, the maximum relative deviation, the average absolute deviation, the average relative deviation, the root mean square error, and the standard deviation of the OCS-PCA-PSO-LSSVM model are significantly lower than the corresponding results of the PCA-PSO-LSSVM model and the SVM model. Where, compared with the PCA-PSO-LSSVM model, the MAXE of the OCS-PCA-PSO-LSSVM model decreased by 39.15%, the MRE decreased by 25.00%, and the STD decreased by 29.89%. The reason for this is that when the PCA-PSO-LSSVM model predicted the 2 nd , 10 th , 20 th , and 21 st sample individuals in the test sample set, their prediction deviations were greater than their respective maximum fitting deviation the training sample set, so the OCS strategy was initiated 4 times to perform the iterative update of the model, therefore, from the 2 nd

SAMSE 2018 IOP Conf. Series: Materials Science and Engineering 490 (2019) 062027

IOP Publishing doi:10.1088/1757-899X/490/6/062027

sample individual of the test sample set containing 25 sample individuals, and the predicted value of the model was different from the predicted value of the PCA-PSO-LSSVM model without the OCS strategy integrated, which was generally reflected as the deviation tends to be small, thus achieving dynamic adjustment and optimization of the model. Tab. 1 Model performances comparison on the testing data set Methods

MAXE /mg·L -1

MAXRE /%

MAE /mg·L -1

MRE /%

RMSE /mg·L -1

STD /mg·L -1

SVM

54.39 51.09 31.09

7.82 7.53 6.26

18.62 17.37 12.31

2.58 2.40 1.80

22.96 21.57 15.23

13.71 13.05

PCA-PSO-LSSVM

OCS-PCA-PSO-LSSVM

9.15

To visually compare the prediction performance of the above three model methods, the experimental values and predicted values of COD of 25 sample individuals in the test sample set are plotted in Fig.5. Through observation of the figure, it can be seen that compared with the PCA-PSO-LSSVM and SVM model methods, the COD results on each sample individual predicted with the OCS-PCA-PSO-LSSVM model method are more closely to their respective experimental values, thereby indicating that the OCS- The PCA-PSO-LSSVM model method has better generalization prediction ability and stronger dynamic stability.

1000 1100 1200 1300

Analysis Value SVM PCA-PSO-LSSVM OCS-PCA-PSO-LSSVM

400 500 600 700 800 900

Observation Number

Fig 5. Prediction results of COD on the generalization data set

4. Conclusions Made in China 2025 clearly pointed out "taking the deep integration of informatization and industrialization as the main line." Based on the safe and healthy management and high-efficiency production requirements in the anaerobic treatment process of papermaking wastewater, this paper focuses on the study of the soft-sensor prediction and dynamic optimization of the model based on the data-driving effluent COD as the water quality indicator, to promote the transformation of the paper industry from extensive development to sustainable development, from the end treatment to the resource utilization, promoting the intelligent management and control of the production process, the main conclusions are as follows: 1) In order to adapt to the structure of anaerobic reactor and the multivariable, nonlinear, time-varying features of the parameters, as well as the special complexity of papermaking wastewater process and the uncertainty of production behavior, the soft-sensor method integrating the modern data analysis technology and intelligent regression model have been developed and designed, which not

SAMSE 2018 IOP Conf. Series: Materials Science and Engineering 490 (2019) 062027

IOP Publishing doi:10.1088/1757-899X/490/6/062027

only effectively reduces the complex collinearity between variables, but also reduces the spatial dimension of the model, and the prediction accuracy and dynamic stability of the model are significantly improved, achieving the overall improvement and breakthrough of the model performance by virtue of the integration advantages. 2) Data-driving soft-sensor model method: As the time series data continues to increase, the prediction accuracy of the model based on long-term historical data will decrease. Taking the actual industrial process as the background, combined with the dynamic change characteristics of the process, the method can adaptively iteratively update the model parameters through deviation feedback, and maintain the generalization performance of the soft-sensor model in real time, thus ensuring the continuous efficient and stable operation of the equipment, and monitoring the energy conservation and emission reduction as well as sustainable development of the enterprise. Acknowledgements Financial supports of this work by National Natural Science Foundation of China (U1609214), Major Projects for Science and Technology Development of Zhejiang Province, China (2015C02037), Zhejiang Science and Technology Program key projects, China (2017C03010), and Zhejiang Province Research Project of Public Welfare Technology Application (2016C33105). References [1]Bourgeois W, Burgess J E, Stuetz R M. 2010. On ϋ line monitoring of wastewater quality: a review[J]. Journal of Chemical Technology & Biotechnology, 76: 337-348 [2]Choi D J, Park H. 2001.A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process[J]. Water Research, 35: 3959-3967 [3]Cristianini N, Shawe T J. 2000. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods[M], Cambridge University Press, 112-120 [4]Dürenmatt D J, Gujer W. 2012. Data-driven modeling approaches to support wastewater treatment plant operation. Environmental Modeling & Software. 30: 47-56 [5]Fortuna L, Graziani S, Rizzo A, et al.2007. Soft Sensors for Monitoring and Control of Industrial Processes[M]. Springer-Verlag: London, 34-45 [6]Haimi H, Mulas M, Corona F, et al. 2013. Data-derived soft-sensors for biological wastewater treatment plants: An overview[J]. Environmental Modelling & Software, 47: 88-107 [7]Huang Mingzhi, Ma Yongwen, Wan Jinquan, et al. 2015. A sensor-software based on a genetic algorithm based neural fuzzy system for modeling and simulating a wastewater treatment process[J]. Applied Soft Computing, 27: 1-10 [8]Jolliffe I T. 2002. Principal Component Analysis (second edition)[M]. Springer-Verlag, 168-176 [9]Kennedy J, Eberhart R C. 1995. Particle swarm optimization[C]. Proceedings of IEEE International Conference on Neural Networks. Perth, Australia, 1942-1948 [10]Langergraber G, Fleischmann N, Hofstaedter F, et al. 2004. Monitoring of a paper mill wastewater treatment plant using UV/VIS spectroscopy[J], Water Science and Technology, 49: 9-14 [11]Liu Bo, Wan Jinquan, Huang Mingzhi, et al. 2015. Online Prediction Model for Effluent VFA from Anaerobic Wastewater Treatment System Based on PCA-LSSVM[J]. Journal of Environmental Sciences, 35(6): 1768-1778 [12]Liu Lin, Ma Yiwen, Wan Jinquan, et al. 2017. Soft-sensor Model of Anaerobic Treatment Process of Wastewater Based on Pso-SVM[J], Journal of Environmental Science, 37(6): 2122-2129 [13]Ruggeri B, Tommasi T, Sanfilippo S. 2015.BioH2 & BioCH4 Through Anaerobic Digestion From Research To Full-Scale Applications[M]. Springer-Verlag, 1-24 [14]Souza F A A, Aráujo R, Mendes J. 2016. Review of soft sensor methods for regression applications. Chemometrics and Intelligent Laboratory Systems, 152: 69-79 [15]Sun Jun, Cheng Zhong, Yang Ruiqin, et al. 2017. PCA-PSO-LSSVM-based Soft-sensor of Effluent COD of the Anaerobic Treatment System for Papermaking Wastewater [J].Computer and Applied Chemistry, 34(9): 706-710 [16]Tang Wei, Bai Zhixiong, Gao Xiang. 2017. Dissolved Oxygen Concentration Control System Based on Adaptive Mutation Differential Evolution Algorithm[J]. Paper China, 36 (6): 49-54

SAMSE 2018 IOP Conf. Series: Materials Science and Engineering 490 (2019) 062027

IOP Publishing doi:10.1088/1757-899X/490/6/062027

[17]Wan Jinquan, Huang Mingzhi, Ma Yongwen, et al. 2011. Prediction of effluent quality of a paper mill wastewater treatment using an adaptive network-based fuzzy inference system[J]. Applied Soft Computing, 11: 3238-3246 [18]Wang Wenchuan, Liu Xinggao.2015.Melt index prediction by least squares support vector machines with an adaptive mutation fruit fly optimization algorithm[J]. Chemometrics and Intelligent Laboratory Systems, 141: 79-87 [19]Wang Yao, Xu Liang, Yin Wenzhi, et al. 2017. Soft-sensor Modeling of Papermaking Wastewater Treatment Process Based on ANN and LSSVR[J]. Transactions of China Pulp and Paper, 32(1): 50-54 [20]Xu Lisha, Qian Xiaoshan. 2012. A Study on Soft-sensor of Effluent COD Based on MUTATION CPSO ALGORITHM[J]. Chinese Journal of Environmental Engineering, 6: 1455-1458 [21]Yang Hao, Mo Weilin, Xiong Zhixin, et al. 2016. Soft-sensor Modeling of Papermaking Wastewater Treatment Process Based on RPLS[J]. Paper China, 35(10): 31-35 [22]Zhang Wei, Liu Min, Chen Wei, et al. 2014. Influence of External Circulation on the Operation Effect of IC Reactors[J]. Journal of Chemical Industry and Engineering, 65: 2329-2334 [23]Zhao Huiru, Huang Guo, Yan Ning. 2018.Forecasting energy-related CO2 emissions employing a novel SSA-LSSVM model: considering structural factors in China[J]. Energies, 11: 781-802. [24]Zheng Rongjian, Pan Feng. 2017. Modeling for Concentration Prediction of Glutamic Acid Fermentation Products based on PLS-LSSVM[J]. Journal of Chemical Industry and Engineering, 68: 976-983 [25]Zhou Hongbiao, Qiao Junfei. 2017. Application of Hybrid Multi-objective Particle Swarm Optimization Algorithm in Optimal Control of Wastewater Treatment Process[J]. Journal of Chemical Industry and Engineering, 68: 3511-3521 [26]Zhou Xinran. 2012. Research on online Modeling and Control Method Based on Least Squares Support Vector Machine[D]. Changsha: Hunan University, 19-21

PAPERmaking! FROM THE PUBLISHERS OF PAPER TECHNOLOGY Volume 5, Number 1, 2019

Biobleaching for pulp and paper industry in India: Emerging enzyme technology Gursharan Singh, Satinderpal Kaur, Madhu Khatri, Shailendra Kumar Arya. Indian pulp and paper industry is one of the fastest emerging business sector of the country which has shown tremendous growth in last few years. Governments policies are creating sustain pressure on paper industries to preserve the clean and pollution free environment at any price. As a result industries are pondering to replace the chemical bleaching processes with facile bio-based cost effective technologies. Eco-friendly bleaching enzymes like xylanases and laccases have the potential for biobleaching of wood and agro-based pulps at industrial scale. In India, enzymatic prebleaching of pulp is widely being investigated and has achieved favourable outcomes but at laboratory scales only and commercial application of enzymes for the delignification of pulp is still at budding stage. This article tends to draw the attention on significant efforts which have been continually attributed by indigenous research laboratories and industries to replace the chemical bleaching with enzymes. Contact information: Department of Biotechnology, University Institute of Engineering Technology, Panjab

University, Chandigarh, India E-mail address: skarya@pu.ac.in Biocatalysis and Agricultural Biotechnology 17 (2019) 558 – 565. https://doi.org/10.1016/j.bcab.2019.01.019

Article 2 – Biobleaching

Page 1 of 9

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Contents lists available at ScienceDirect

Biocatalysis and Agricultural Biotechnology

journal homepage: www.elsevier.com/locate/bab

Biobleaching for pulp and paper industry in India: Emerging enzyme technology Gursharan Singh, Satinderpal Kaur, Madhu Khatri, Shailendra Kumar Arya ⁎ Department of Biotechnology, University Institute of Engineering Technology, Panjab University, Chandigarh, India

ARTICLE INFO

A B S T R A C T

Indian pulp and paper industry is one of the fastest emerging business sector of the country which has shown tremendous growth in last few years. Governments policies are creating sustain pressure on paper industries to preserve the clean and pollution free environment at any price. As a result industries are pondering to replace the chemical bleaching processes with facile bio-based cost e ff ective technologies. Eco-friendly bleaching enzymes like xylanases and laccases have the potential for biobleaching of wood and agro-based pulps at industrial scale. In India, enzymatic prebleaching of pulp is widely being investigated and has achieved favorable outcomes but at laboratory scales only and commercial application of enzymes for the deligni fi cation of pulp is still at budding stage. This article tends to draw the attention on signi fi cant e ff orts which have been continually attributed by indigenous research laboratories and industries to replace the chemical bleaching with enzymes.

Keywords: Biobleaching Eco-friendly Laccase Pulp and paper Xylanase

1. Introduction

Bajpai, 2012). The high organic content (especially in the wood based pulp), coupled with chlorine dioxide used in the bleaching process, results in the production of organo-chlorine compounds, which are fi - nally discharged as bleach e ffl uents in water bodies. These organo- chlorine compounds (measured as Adsorbable Organic Halogens, AOX) have been reported to cause genetic and reproductive damages in aquatic as well as terrestrial animals including humans (Sharma et al., 2014). Although more eco-friendly options for bleaching are open to pulp mills in the form of alternatives to ClO 2 like extended cooking or oxygen, hydrogen peroxide or ozone based deligni fi cation, but im- plementation of these alternates needs process modi fi cations and is considered as cost intensive proposition at large scale. Enzymes provide a simpler and cost e ff ective way to reduce the use of ClO 2 , chlorine compounds and other bleaching chemicals. Enzymes also o ff er the simple approach that allows for a higher brightness ceiling to be reached (Abhay et al., 2018). This can all be achieved without major capital investment. The applications of xylanase enzyme as pre- bleaching agent has been established in several laboratories and has also been commercially exploited in Europe, North America and in few Asian countries (Bajpai, 2012).

Currently Indian pulp and paper industrial units account for ~ 3.0% of the world's production of paper. The estimated turnover of the in- dustry is US$ ~ 8.0 billion. The industry provides employment to more than 0.5 million people directly and 1.5 million indirectly. During 2015 – 16, domestic production of paper was estimated to be 12.2 mil- lion tons (http://ipma.co.in). Paper industry in country is becoming more promising as the domestic demand of paper is increasing due to the growing population and literacy rate, growth in gross domestic product (GDP) and lifestyle of the individuals (Sharma et al., 2015a; Sharma et al., 2015b; Sharma et al., 2015c). The focus of paper industry is now shifting towards eco-friendly production of paper. The paper is produced from pulps generated from wood, agricultural residues like wheat straw or from waste paper. The use of wood based technology is constantly on the decline because of capital and raw material avail- ability constraints. The production of pulp and paper involves three important steps viz. pulping, bleaching, and fi nal paper fi nishing. The removal of recalcitrant lignin from pulp is called bleaching which is necessary for making the bright and white paper. Till the end of 20th century, bleaching of pulps, irrespective of their origin from soft or hard wood, employed large amounts of chlorine and chlorine based chemi- cals. But now most of the pulp and paper mills worldwide use chlorine dioxide (ClO 2 ) as the elemental chlorine free (ECF) bleaching agent for the production of high quality white paper (Dwivedi et al., 2010;

2. Structure of the Indian paper industry

The Indian paper industry recognized as the aggregation of small, medium and large sized paper mills with di ff erent paper making

⁎ Corresponding author. E-mail address: skarya@pu.ac.in (S.K. Arya).

https://doi.org/10.1016/j.bcab.2019.01.019 Received 8 November 2018; Received in revised form 9 January 2019; Accepted 10 January 2019

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G. Singh et al.

capacities, 10 – 1150 t per day. Paper production in the country is widely based on wood and agricultural waste as the major raw materials. The Indian paper industry prominently produces writing, newsprint and commercial grade paper. Newsprint grade paper is produced by mills utilizing mainly of recycled waste paper as the raw material. In 2012, India recorded the paper consumption of 9.3 kg/capita besides global average was 58 kg/capita. Presently there are 759 paper mills in the country and producing ~ 10.9 Mt of paper annually (http://psa.gov. in;initiatives-pulp-and-paper-industry-2014). Indian paper manu- facturers association (IPMA) representing the platform to project paper industry's views and articulate its strategies. IPMA promoted the in- terests of paper industry in the country and help it achieve global competitiveness while striving to be an active participant in the policy making process. The important activities of IPMA are following, work as the interface with government, non-governmental organizations (NGOs) and industrial associations so as to present the perspective and interests of Indian paper mills. Promote the excellence in paper man- ufacturing through presentation of awards, networking with interna- tional bodies with a view to gain better visibility for Indian paper in- dustry. IPMA also synchronize the R&D projects in collaboration with academic institutions of India.

3.1. Pulping

Pulping is the fi rst step of paper making procedure in which se- paration of cellulose fi bers from the lignin components. Commonly two di ff erent methods of pulping are applying in the Indian pulp and paper industries, chemical pulping and chemi-mechanical pulping.

3.2. Chemical pulping - Kraft sulphate process

In this procedure the wood chips usually cooked at higher tem- perature, 165 – 170 °C in the presence of sodium hydroxide (caustic soda) and sodium sulphide to separate the lignin and wood resins from the cellulose. About 92 – 95% of the chemicals (sodium hydroxide, so- dium sulphide and lime) can be recovered and reuse further.

3.3. Chemical pulping – soda process

The soda pulping is used for the conversion of agro residues (like wheat and rice straw and bagasse) to pulp. In this case raw materials usually cooked in the presence of caustic soda at a temperature of 150 – 160 °C to separate lignin from the cellulosic material.

3. Manufacturing process of paper in Indian paper mills

3.4. Chemi-mechanical pulping (CMP)

The manufacturing process of paper industry can be divided in to three steps, pulping, bleaching and papermaking. Among all of the three steps, bleaching is tedious and combination of chemical and physical treatment of lignin contained pulp (Fig. 1).

In the chemi-mechanical pulping the wood chips initially treated with the mild caustic soda based chemicals to extract resin and lignin from the cellulose prior to mechanical re fi ning.

Fig. 1. Common manufacturing process of paper in Indian paper mills.

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G. Singh et al.

of eucalyptus kraft pulp with 31% reduction in chlorine consumption (Sindhu et al., 2006). Extracellular cellulase free xylanase produced from Bacillus subtilis C01 increased the brightness by 19% of banana pulp Ayyachamy and Vatsala (Ayyachamy and Vatsala, 2007). Puri fi ed alkali stable xylanase from Aspergillus fi scheri was immobilized on polystyrene that reduced the kappa number of paper pulp by 87% (Senthilkumar et al., 2008). A synergistic action of xylano-pectinolytic enzymes from Bacillus pumilus was evaluated for the prebleaching of kraft pulp; as a result 8.5% and 25% reduction was noticed in kappa number and chlorine consumption respectively (Kaur et al., 2010). Alkali stable and thermo tolerant xylanase from B. pumilus SV-85S showed (at pH 9.0, 55 °C for 2.0 h) the reduction in kappa number by 1.6 points and increased brightness by 1.9 points. The pretreatment of pulp with xylanase resulted in 29% reduction in chlorine consumption (Nagar et al., 2013). First report on a bacterial system involving direct growth of xylanase -producing B. halodurans FNP 135 on kraft (eu- calyptus) pulp under submerged fermentation conditions, showed 35% reduction in kappa number and 5.8% enhancement in brightness with 20% reduction in chlorine consumption (Gupta et al., 2015). Kumar et al. (2016) emphasized that signi fi cant application of thermostable xylanases is biobleaching in pulp and paper industry, where these en- zymes acted as delignifying agents, showing clear economic and en- vironmental advantages over chemical alternatives. After xylanases, laccases are the next extensively explored enzymes for biobleaching of pulp; these are oxidative biocatalysts that have in fl uenced the re- searchers by their numerous merits over any other bleaching enzyme (Singh et al., 2008; Singh et al., 2010; Singh et al., 2009; Singh et al., 2015). Laccases, together with mediators are able to delignify the pulp by the oxidation chain reaction leading to lignin oxidation without the degradation of cellulose. In India pioneering work on alkalophilic lac- cases was started by Bains et al. (Bains et al., 2003), through isolation of a novel strain named as γ -proteobacterium JB. An alkalophilic cellu- lase-free laccase from γ -proteobacterium JB was applied to wheat straw-rich soda pulp to evaluate its bleaching potential by optimizing the conditions statistically using response surface methodology based on central composite design in the presence of ABTS at pH 8.0 which enhanced the brightness by 5.8 and reduced the kappa number by 21% within 4 h of incubation at 55 °C. It was noticed that pre-bleaching of eucalyptus kraft pulp with xylanase or laccase individually avoided the ClO 2 by 15% and 25% respectively. When both enzymes were applied together at pilot scale (50 kg pulp), there was reduced organo-chlorine compounds consumption by 34% in bleach e ffl uent (Sharma et al., 2014). Tables 1, 2 shows the year wise isolation of new laccase and xylanase producing organisms and enzyme characterization, but there were very few enzymes either xylanase or laccase evaluated for bio- leaching of pulps. Recently, also many reports published on xylanases and laccases from Indian laboratories but none of them studied on deligni fi cation of biomass (Sharma et al., 2015a; Sharma et al., 2015b; Sharma et al., 2015c; Desai and Iyer, 2016; Nikam et al., 2017; Afreen et al., 2017; Dharmesh et al., 2017; Raj et al., 2018; Kumar et al., 2018; Ranimol et al., 2018).

3.5. De-inking of RCF

Recycled fi bers (RCF) dispersion or fl oatation pulping process is applied for the de-inking of the news papers/print papers. For de- inking, chemicals such as detergents, dispersants and foaming agents added and ink is separated from the pulp.

3.6. Pre-bleaching of pulp with enzymes

The term bleaching is generally referred to the removal of lignin from any kind of the pulp by use of chemicals/gases/steam etc. but prebleaching terminology is used for the enzymatic treatment of the pulp for removal of lignin. Prebleaching is an eco-friendly and cleaner process of lignin removal that can save the chlorine based and other chemicals 10 – 15% (Bajpai, 2004; Camarero et al., 2007; Garg et al., 2011). Prebleaching of pulp with enzymes is still under trial or at pilot scale in paper mills of India.

3.7. Chlorine bleaching of pulp

The process is used to remove the residual lignin in the range 5 – 10%. This process is followed by several stages of treatment of pulp with chlorine dioxide or hypochlorite to whiten the pulp. Bleaching process employed in most of the medium and small mills is based on elemental chlorine. However, few of the large sized wood based/agro based mills have introduced elemental chlorine free (ECF) bleaching process making use of chlorine dioxide ClO 2 .

3.8. Elemental chlorine free (ECF) bleaching

ECF bleaching technology is being practiced in few large mills of the country where it uses oxygen deligni fi cation (ODL), followed by ClO 2 to enhance the brightness of the pulp.

4. Eco-friendly bleaching enzymes (xylanases and laccases) studied by the Indian research laboratories

There are numerous commercially available enzyme cocktails are available, but due to the di ff erences in paper making process in the developed countries and in India, it has been felt to characterize en- zymatic pre-bleaching process indigenously with enzymes produced from locally isolated cultures or with commercially available enzymes that match with the interests of Indian pulp industries. One of the major di ff erences is the use of di ff erent sort of raw materials for pulp making in India (Sharma et al., 2015a; Sharma et al., 2015b; Sharma et al., 2015c; Dutt et al., 2009; Bajpai et al., 1994; Singh et al., 2008; Singh et al., 2010). Up to the 1980, there was no university or institute was associated in research and development (R&D) that can directly in- volved for giving the technical guidelines to Indian paper industry. R&D progress on enzymes for paper industry is still in its beginning and only single institute works in a direction to undertake industry related issues and emphasized on applied research, is Central Pulp and Paper Re- search Institute (CPPRI). There were only a few reports on xylanases for the biobleaching of pulp in country before 2000, e.g. treatment of eu- calyptus pulp with commercial xylanases such as Novozyme 473, and Cartazyme HS-10 reduced the chlorine consumption by 31% and in- creased the fi nal brightness by 2.1 – 4.9 points (Bajpai et al., 1994). Thermostable cellulase-free xylanase from Streptomyces sp. QG-11-3 was produced and applied for deligni fi cation of eucalyptus kraft pulp at pH 8.5 and 50 °C for 2 h. There was reduction in kappa number and increase in brightness of pulp by 25% and 20% respectively (Beg et al., 2000). Bajpai, reported, properties of many commercial xylanases make them unsuitable for the real process of pulp bleaching (Bajpai, 2004). So industries need xylanases which can function e ffi ciently in their existing papermaking processes. Xylanase from Bacillus megaterium showed 8.1% decrease in kappa number and 13% increase in brightness

5. Commercial use and availability of Indian patents on bleaching enzymes

R&D work on isolation and screening of microbial cultures, capable of producing low molecular weight xylanases was started initially at National Chemical Laboratory Pune in early 1990s. Later, IIT Delhi, Birla Institute of Scienti fi c and Industrial Research Jaipur and few other research and academic institutions began working on culture develop- ment for the production of alkaline thermo-tolerant xylanase enzymes. A national research laboratory CPPRI and a premier educational in- stitution in the country, Institute of Paper Technology (IPT) also in- itiated R&D on xylanase enzyme based pre-bleaching of the pulp. The fi rst ever mill trial of xylanase pre-bleaching in India was conducted in a pulp and paper mill of Ballarpur Industries Ltd. (BILT) in 1992 using

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