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Bisphenol A, a toxic chemical, is widely used in the preparation of epoxy resins. Therefore, the use of harmless biomass-based raw materials to replace bisphenol A in the preparation of epoxy resin has attracted extensive attention. A demethylated-lignin-based epoxy resin adhesive was prepared. After lignin was modified in a LiBr/HBr system, the phenolic hydroxyl group content of the obtained demethylation lignin (DL) increased to 6.19 mmol g−1, and the tensile strength of the as-prepared DL-modified adhesive (–OH content of the DL = 6.19 mmol g−1) increased to 62.50 MPa, which was much higher than the adhesive modified by raw lignin (38.89 MPa). Due to its excellent crosslinking density, the DL-modified adhesive retained more than 85% of its tensile shear strength performance after being soaked in water for 48 h or frozen at −25 °C for 48 h. Therefore, this adhesive has a wide range of application prospects in furniture and plywood manufacturing. [ABSTRACT FROM AUTHOR]

As a green adhesive, lignin-based acrylate adhesives have a promising future in substitute commercial adhesives owing to their cost-effectiveness, and environmental friendliness. In this work, a green and convenient strategy for the fabrication of lignin-based acrylate adhesives was developed via the introduction of lauryl methacrylate with a low glass transition temperature (−62 °C) into the chemical structure of lignin to improve the compatibility, toughness, strength and wettability of lignin-based adhesives. With the introduction of lauryl methacrylate, the shear strength of the lignin-based adhesive reached 5.10 MPa, which is 114% higher than the traditional acrylate adhesive. Moreover, the lignin-based adhesive also exhibits excellent hydrophobicity and high shear strength after 12 h of immersion in water. This work not only provides a new strategy for the fabrication of economical, green, and sustainable lignin-based acrylate adhesives without the participation of organic solvents, but also achieves the value-added utilization of lignin. [ABSTRACT FROM AUTHOR]

With the development of the customization concept, small-batch and multi-variety production will become one of the major production modes, especially for fast-moving consumer goods. However, this production mode has two issues: high production cost and the long manufacturing period. To address these issues, this study proposes a multi-objective optimization model for the flexible flow-shop to optimize the production scheduling, which would maximize the production efficiency by minimizing the production cost and makespan. The model is designed based on hybrid algorithms, which combine a fast non-dominated genetic algorithm (NSGA-II) and a variable neighborhood search algorithm (VNS). In this model, NSGA-II is the major algorithm to calculate the optimal solutions. VNS is to improve the quality of the solution obtained by NSGA-II. The model is verified by an example of a real-world typical FFS, a tissue papermaking mill. The results show that the scheduling model can reduce production costs by 4.2% and makespan by 6.8% compared with manual scheduling. The hybrid VNS-NSGA-II model also shows better performance than NSGA-II, both in production cost and makespan. Hybrid algorithms are a good solution for multi-objective optimization issues in flexible flow-shop production scheduling. [ABSTRACT FROM AUTHOR]

This study proposes a data-driven model for monitoring and diagnosis in energy system of papermaking process based on kernel component analysis (KPCA) and the kernel slow feature analysis (KSFA). Four different abnormal patterns are designed, while the false alarm rate (FAR) and the missed detection rate (MDR) are used to evaluate the validity of the proposed model. The performance of KPCA is better than that of conventional PCA and KSFA. The online monitoring and diagnosis analysis of energy utilization are achieved based on the historical data and the proposed model. The production status and the energy consumption level across the whole process can be acquired. The results demonstrate that the proposed method is effective. It can provide the valuable reference foundation for further energy analysis and optimization in papermaking process. [ABSTRACT FROM AUTHOR]

Papermaking is a highly energy-consuming industry. The growth in paper demand will further intensify the need for energy and the stress of GHG emissions. Papermaking involves complex processing routes, and energy is required for collecting raw materials, producing chemicals, and pulp and papermaking. Previous investigations of energy consumption in the papermaking industry have focused primarily on the analysis of one single product or one single pathway, lacking a comprehensive and systematic comparison of various products and pathways. Herein, the results of a life cycle energy consumption analysis of major paper products in China using an extensive system boundary are presented. When 1 tonne of paper was produced in China in 2015, the maximum energy consumption was 38.17 GJ for tissue paper, with a minimum energy consumption of 15.90 GJ for corrugated medium. This study also predicts energy-related GHG emissions and a mitigation target in the papermaking industry in China by 2050. Although the energy consumption assumed to be distributed to the papermaking industry can meet its development requirements, the GHG emissions will exceed the mitigation target by about 101 Mt CO2 eq. in 2050 under current policy. The target can be achieved by changing the energy structure and promoting low-carbon technology. [ABSTRACT FROM AUTHOR]

This study presents an integrated approach to assess energy performance during hood retrofitting from a semi-closed hood to a closed hood. According to the principle of heat and mass transfer, a simulation program was established by a mathematical model and some necessary model parameters. It could be used to simulate the paper-drying process and calculate steam and power consumption during paper drying. The method was applied to a corrugated paper machine. After a fundamental field test and observation, the simulation program determined appropriate model parameters. Finally, we found a 13.2% steam consumption reduction and a 13.5% total energy consumption reduction. [ABSTRACT FROM PUBLISHER]

The pulp and paper sector is the fourth largest industrial sector in terms of energy use in the world. Of the numerous processes involved in paper-making by a paper machine, the dryer section is the process that consumes the largest amount of energy. A model-based method for estimating energy-saving potentials of the dryer section was put forward in the present study. It was done by four steps: establishing a mathematical model about energy consumption, determining the model parameters, benchmarking the drying performance to obtain the corresponding energy-saving measures, and estimating the energy-saving potentials by using the mathematical model. In a case study, a multi-cylinder dryer section was selected to illustrate the method. After a fundamental field test and observation, several operating problems that restricted the energy performance of paper drying were found. And then several reasonable energy-saving measures were suggested to the operators. Finally, applying the mathematic model, it was found that 0.32 ton of steam will be saved when producing 1 ton of paper in recommended operating conditions. With the designed capacity of 200,000 tons/year, the annual steam-savings will be 64,000 tons. Generally, the price of steam is 130–150 Chinese Yuan (about US$21–24) in China, and the annual economic benefits will be 8.32–9.60 million Chinese Yuan (about US$1.344–1.536 million). [ABSTRACT FROM AUTHOR]

Paper drying is a highly energy-intensive and complicated multivariate process. The dryer section plays an important role in the energy consumption of a paper machine, especially of thermal energy. A comprehensive method for assessing the energy performance of the dryer section was investigated in this study to improve energy efficiency. This method was divided into three component processes: energy and evaporation load audit, field test and observation, and energy flow analysis and energy efficiency estimation. In a case study, we found that the method could, in addition to analyzing the key factors that restrict drying efficiency, also depict the details of energy consumption clearly. At the same time, several significant energy-saving measures were suggested to improve the energy efficiency of the paper-drying process. [ABSTRACT FROM PUBLISHER]

Abstract: The systematic, transparent and accurate on-line energy information of the production process is quite the basis of mill energy management and conservation. An Energy Management System (EMS) was built according to the characteristics of the energy usage in a typical newsprint paper mill. The EMS first realized the process data acquisition and integration, second fulfilled the on-line energy information calculation from the integrated data. A water and steam properties query component based on IAPWS-IF97 was developed and plugged into the EMS to facilitate the energy calculation of water and steam. The practical performance in the paper mill showed that the integrated energy information of the whole mill was gained and the on-line energy supervisory was achieved. In the end a systematic, transparent and accurate energy usage profile is obtained and it would provide the valuable fundamental energy data for the further energy analysis and optimization in the EMS. [Copyright &y& Elsevier]

Due to high energy consumption in the Chinese paper industry, this study considers higher-energy efficiency for the multicylinder dryer section of paper machines. A common situation in the Chinese paper industry is that energy is consumed in extensive mode. In order to improve the energy efficiency of the paper machine dryer section, deeper analysis and optimization of process parameters are necessary. A NLP optimization method is developed for integration of steam system and air system to reduce the steam consumption and decrease the loads of centrifugal blowers in the multicylinder dryer section of a paper machine. Equality constraints of the optimization model are extracted from different process modules based on material and energy balance. Inequality constraints are from the production capacity, operating condition, etc. Two illustrative examples are presented in this paper. The results show that the optimization model is adaptive and convenient for application. For a newsprint machine, less dry air and steam are used and the energy consumption can be reduced by about 8% in the dryer section. Applied on a linerboard machine which has surface sizing, the method can reduce the energy consumption by 5.6%. [ABSTRACT FROM AUTHOR]

The paper sheet drying process consumes about 70% of the total energy required in coated papermaking, and almost all the thermal energy used in the process can be found in the exhaust air; thus, it has significant potential to recover the heat. With the aim of saving energy, the recovered energy is usually used to heat different process streams instead of steam. This article examines the drying process of an operating coating paper machine to demonstrate an optimization method. To study the possibility of improving energy efficiency, thermodynamic analysis was conducted. The reasons why there is so much heat lost during drying were investigated. Based on the results of the energy and exergy analysis, a new waste heat integration scheme is presented. Furthermore, the performance of the proposed scheme has been evaluated. The results of the case study show an energy efficiency improvement of 7.3% and a specific energy consumption reduction of 4.6% with profitable investments. [ABSTRACT FROM AUTHOR]

Renewable energy represented by wind energy and photovoltaic energy is used for energy structure adjustment to solve the energy and environmental problems. However, wind or photovoltaic power generation is unstable which caused by environmental impact. Energy storage is an important method to eliminate the instability, and lithium batteries are an increasingly mature technique. If the capacity is too large, it would cause waste and cost would increase, but too small capacity cannot schedule well. At the same time, the size of energy storage capacity is also constrained by power consumption, whereas large-scale industrial power consumption is random and non-periodic. This is a complex problem which needs a model that can not only dispatch but also give a reasonable storage capacity. This paper proposes a model considering the cycle life of a lithium battery and the installation parameters of the battery, and the electricity consumption data and photovoltaic power generation data of an industrial park was used to establish an energy management model. The energy management system aimed to reduce operating costs and obtain optimal energy storage capacity, which is constrained by lithium battery performance and grid demand. With the operational cost and reasonable battery capacity as the optimization objectives, the Deep Deterministic Policy Gradient (DDPG) method, the greedy dynamic programming algorithm, and the genetic algorithm (GA) were adopted, where the performance of lithium battery and the requirement of power grid were the constraints. The simulation results show that compared with the current forms of energy, the three energy management methods reduced the cost of capacity and operating of the energy storage system by 18.9%, 36.1%, and 35.9%, respectively. [ABSTRACT FROM AUTHOR]

With the global energy crisis and environmental pollution intensifying, tissue papermaking enterprises urgently need to save energy. The energy consumption model is essential for the energy saving of tissue paper machines. The energy consumption of tissue paper machine is very complicated, and the workload and difficulty of using the mechanism model to establish the energy consumption model of tissue paper machine are very large. Therefore, this article aims to build an empirical energy consumption model for tissue paper machines. The energy consumption of this model includes electricity consumption and steam consumption. Since the process parameters have a great influence on the energy consumption of the tissue paper machines, this study uses three methods: linear regression, artificial neural network and extreme gradient boosting tree to establish the relationship between process parameters and power consumption, and process parameters and steam consumption. Then, the best power consumption model and the best steam consumption model are selected from the models established by linear regression, artificial neural network and the extreme gradient boosting tree. Further, they are combined into the energy consumption model of the tissue paper machine. Finally, the models established by the three methods are evaluated. The experimental results show that using the empirical model for tissue paper machine energy consumption modeling is feasible. The result also indicates that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The experimental results show that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The mean absolute percentage error of the electricity consumption model and the steam consumption model built by the extreme gradient boosting tree is approximately 2.72 and 1.87, respectively. The root mean square errors of these two models are about 4.74 and 0.03, respectively. The result also indicates that using the empirical model for tissue paper machine energy consumption modeling is feasible, and the extreme gradient boosting tree is an efficient method for modeling energy consumption of tissue paper machines. [ABSTRACT FROM AUTHOR]

To elucidate the effects of extracellular polymeric substance (EPS) on the sludge dewaterability, this study comparatively investigated the changes in EPS composition and spatial distribution, together with the sludge dewaterability after lysozyme (LZM) conditioning. The protein concentration in the tightly bound EPS (TB-EPS) increased from 3.47 mg g−1 DS to 4.99 mg g−1 DS within the first 2 min, then gradually decreased, which could be described by a piecewise linear function. Unlike TB-EPS, the protein content variation trend in both soluble EPS (S-EPS) and loosely bound EPS (LB-EPS) followed the typical first-order kinetics. Additionally, the extended DLVO theory was employed in combination with viscoelastic acoustic response analysis to further explore the impact of EPS composition on water adhesion and microbial cell. After the extraction of S-EPS from the conditioned sludge, the adsorption free energy (ΔG adh) of EPS ascended to −61.05 mJ m−2, indicating the weakened microbial hydrophobicity. By contrast, the ΔG adh value declined after the subsequent extraction of LB-EPS and TB-EPS. Meanwhile, the adsorption potential energy between S-EPS and microbial cells showed an increasing trend, whereas the repulsion potential energy between TB-EPS and microbial cells fell to 1.40 × 104 kT, signifying a weakened adsorption capacity to water. Accordingly, the viscosity and shear modulus of each EPS layer were reduced after conditioning, which contributed to the transformation of bound water into free water. These changes reasonably explained the results that the water content in the dewatered sludge after conditioning was reduced to 58.54%, and the bound water content decreased by 15.06%. Image 1 • The change of TB-EPS was described by piecewise function after LZM conditioning. • Protein substances in EPS were largely responsible for sludge dewatering. • The hydrophobicity of the enzyme-treated sludge was improved. • Reduction of repulsion between TB-EPS and cell weaken the absorption of EPS to water. • The reduction of EPS viscosity and shear modulus was conducive to sludge dewatering. [ABSTRACT FROM AUTHOR]

Recovered paper is one of the major raw material for recycled papermaking industry. In China, the domestic recovered paper recycling system is incomplete and the amount of recyclable recovered paper cannot satisfy the papermaking demand. Therefore, the recycled papermaking industry is extremely dependent on imported recovered paper as their raw fiber source. In 2018, Chinese government announced a ban on unsorted recovered paper and tightened the import quota for recovered paper, which resulted in a dramatic drop in the importing amount of recovered paper. It is an urgent issue to explore alternative production routes that are more economical and environmentally friendly. Herein, life cycle cost assessment is used to compare and analyze the recycled papermaking industry based domestic recovered paper and three alternatives (including straw, imported deinked pulp and imported wood pulp) as the raw materials. The results show that the life cycle cost of domestic recycled paper based recovered papermaking is significantly lower than the other three alternatives, whereas its external cost is higher than that of the production routes based on the imported deinked pulp and imported wood pulp. With the increasing price of the domestic recovered paper, imported deinked pulp is the trend of recycled papermaking industry in China. • Life cycle cost assessment is applied to the recycled paper industry in China. • LCC of four papermaking schemes based on different raw materials were assessed. • LCC for six types of paper products ranges from 924 to 1227 USD/t in 2017. • Imported recovered pulpboard is the best choice of wastepaper material supplement. [ABSTRACT FROM AUTHOR]

Extracellular polymeric substance (EPS) and the water within it account for about 80% of the total sludge mass, significantly correlated with sludge charge properties, particle size, and dewaterability, while their relation is still ambiguous. To clarify the effect of EPS characteristics on the activated sludge dewaterability, the sludge conditioned by cationic polyacrylamide (CPAM) and lysozyme alone or in combination was comparatively investigated by the means of the SPSS17 software incorporation with chemical analysis. According to the results, the combined conditioning increased both dewatering extent and dewatering rate with the water content as low as 57.79%. It was mainly attributed to the destruction of microbial cell wall and EPS structure by enzymatic conditioning, beneficial for the release of protein (PN) and polysaccharide (PS), as well as the conversion of intracellular water and some bound water into free water. Additionally, Pearson's correlation and factor analysis confirmed the significant influence of EPS properties on sludge dewaterability and explored their detailed relationship. It was indicated the most crucial factors consisted of PNS (PN in S-EPS), PNL (PN in LB-EPS), PNT (PN in TB-EPS), and PST (PS in TB-EPS) accounted for 72.83% in all of the total variance for the contribution to the dewatered water content. Moreover, the high concentration of PNS and PNL led to the zeta potential rising to −9.74 mV, and the destruction of EPS structure was favorable for sludge to form smaller particle size and compact floc structure. All the results were confirmed by the microstructure changes of the sludge flocs. Image 1 • LZM-CPAM conditioning improved the dewatering rate and extent of activated sludge. • Factor analysis provided a new insight into the role of EPS in sludge dewatering. • PNS, PNL, PNT, and PST were of significant importance to the sludge dewaterability. [ABSTRACT FROM AUTHOR]

Papermaking industry can hardly monitor the dynamic of certain variables in the process when the test can only be conducted offline or destroy samples. Soft sensor is a predictive model that maps the measurable variables to the unknown measurements. In order to address the caused uncertainty, promote the papermaking process, a couple of soft sensors are developed with the industrial data by means of random forest (RF), gradient boosting regression (GBR), ridge regression and K-nearest neighbor (KNN) to monitor folding endurance, bursting strength, smoothness, and transverse ring compressive strength. The optimal models hold accuracy ≥0.839 (R-square) in general, and are applied to the multi-objective of cleaner papermaking production with regard to cost, energy consumption, and greenhouse gas (GHG) emission. The optimized results show that, when process is qualified with soft sensors' support, the possible reduction of cost, energy consumption and GHG emission could be up to 17.3% in total. [ABSTRACT FROM AUTHOR]

Papermaking is a water intensive industry. The growth of paper demand will further intensify the need of fresh water resources. Papermaking involves complex processing routes. Previous investigations of water consumption in papermaking industry have focused primarily on key processes and pathways, ignoring the impacts of many intermediate and inter-related processes in paper production cycles and there underestimating the sustainability impacts. Herein, the results of a life cycle analysis of water consumption for papermaking industry in China using an extensive system boundary that includes the water embedded in intermediate processing are presented. The results show the life cycle water consumption and water saving potentials for different papermaking pathways. The advocacy of "forest pulp and paper integration" may have few practical contribution to reducing water consumption due to the low indirect water consumption of raw materials and energy for transportation. [ABSTRACT FROM AUTHOR]

Electricity consumption and material wastage of manufacturing industries have directly or indirectly influenced the environment. Many manufacturing enterprises are committed to improving energy and material efficiencies. To achieve this goal, one of the high-efficiency ways is to optimize the scheduling. In this study, a multi-object optimization model is constructed for a particular kind of batch processing scheduling problem existed in flexible flow shops, where the objects include makespan, electricity consumption and material wastage. First, the models for electricity consumption and material wastage are built, based on which a mathematic model for scheduling problem is built afterwards. Finally, a hybrid non-dominated sorting genetic algorithm II (NSGA-II) method is employed to solve the multi-object optimization scheduling model. A case study based on a real-world paper mill industry is presented to demonstrate the effectiveness of the method and its application in practice. Moreover, a real-life scheduling problem is investigated, and the result shows that the proposed method is more efficient than manual scheduling in energy and material saving. In fact, many tissue paper mills or other manufacturing industries schedule by manual labor in China, indicating that the proposed method has a significant potential in improving energy and material efficiencies in the studied flexible flow shop, and in energy and material saving in similar industries. [ABSTRACT FROM AUTHOR]

Abstract: The pulp and paper industry is one of the most energy-intensive industries worldwide. In 2007, it accounted for 5% of total global industrial energy consumption and 2% of direct industrial carbon dioxide (CO2) emissions. An energy audit is a primary step toward improving energy efficiency at the facility level. This paper describes a plant-wide energy audit aimed at identifying energy conservation and CO2 mitigation opportunities at a paper mill in Guangdong province, China. We describe the energy audit methods, relevant Chinese standards, methods of calculating energy and carbon indicators, baseline energy consumption and CO2 emissions of the audited paper mill, and nine energy-efficiency improvement opportunities identified by the audit. For each of the nine options, we evaluate the energy conservation and associated CO2 mitigation potential. The total technical energy conservation potential for these nine opportunities is 967.8terajoules (TJ), and the total CO2 mitigation potential is equal to 93,453tonnes CO2 annually, representing 14.4% and 14.7%, respectively, of the mill’s total energy consumption and CO2 emissions during the audit period. [Copyright &y& Elsevier]