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Hydrogen energy is widely regarded as one of the most promising clean energy sources for carbon emissions reduction due to its high-grade energy and complete clean combustion. Since the mechanism of the production process is difficult to elucidate and efficient production cannot be maintained at all times, it has the disadvantages of instability and low efficiency of production processes. The forecasting results can help the biomass based hydrogen production process to adjust the material input in time, thus ensuring a stable and efficient production. Therefore, to solve those problems, the dynamic hydrogen production yield forecasting model based on the Discrete Grey Method (DGM) and the Gradient Boosting Regression Tree (GBRT) has been proposed. The results show that the forecasting results of the GBRT based model have a strong correlation with the input data, while the DGM based model can eliminate some unreasonable forecasting results. Thus, the proposed model can get good forecasting results in different scenarios since its MAE is 0.2. To verify the proposed model, five widely used forecasting models are used as the contrasting models. The forecasting results show that it has the highest precision and has no uncertainty results. [Display omitted] • Dynamic forecasting model is proposed for biomass-based hydrogen production yield. • The proposed model is developed based on GBRT-DGM (1, n) algorithm. • The model shows high accuracy and has no uncertain results. • The accuracy of the proposed method is compared with other models. [ABSTRACT FROM AUTHOR]

Abstract Process industry consumes tremendous amounts of electricity for production. Electric load forecasting could be conducive to managing the electricity consumption, determining the optimal production scheduling, and planning the maintenance schedule, which could improve the energy efficiency and reduce the production cost. This paper proposed a short term electric load forecasting model based on the hybrid GA-PSO-BPNN algorithm. The GA-PSO algorithm is used in a short-term electric load forecasting model to optimize the parameters of BPNN. The forecasting model avoids the shortcoming that the prediction result is easy to fall into local optimum. The papermaking process, as one of the most representative process industries, is selected as the study case. The real-time production data from two different papermaking enterprises is collected to verify the proposed model. Besides the proposed GA-PSO-BPNN model, the GA-BPNN and PSO-BPNN based electric load forecasting models are also studied as the contrasting cases. The verification results reveal that the GA-PSO-BPNN model is superior to the other two hybrid forecasting models for future application in the papermaking process since its MAPE is only 0.77%. Highlights • A short term electric load forecasting model is proposed for papermaking process. • The proposed model is structured based on hybrid GA-PSO-BPNN algorithm. • The model is verified by industrial real-time data. • The model shows high accuracy with 0.77% of the MAPE. • Electricity purchasing cost can be saved by the forecasting results. [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]

Forecasting accuracy electricity load can help industrial enterprises optimise production scheduling based on peak and off-peak electricity prices. The electricity load forecasting results can be provided to an electricity system to improve electricity generation efficiency and minimize energy consumption by developing electricity generation plans in advance and by avoiding over or under the generation of electricity. However, because of the different informatization levels in different industries, few reliable intelligent electricity management systems are applied on the power supply side. Based on industrial big data and machine learning algorithms, this study proposes an integrated model to forecast short-term electricity load. The hybrid model based on the hybrid mode decomposition algorithms is proposed to decompose the total electricity load signal. To improve the generalisation ability of the forecasting model, a dynamic forecasting model is proposed based on the improved hybrid intelligent algorithm to forecast the short-term electricity load. The results show that the accuracy of the proposed dynamic integrated electricity load forecasting model is as high as 99%. The integrated framework could forecast abnormal electricity consumption in time and provide reliable evidence for production process scheduling. • Forecasting models are too difficult to embedded in industrial process systems. • The novel integrated framework has been proposed for industrial processes. • The proposed integrated model has been implemented in papermaking enterprise. • The accuracy of the proposed dynamic load forecasting model is greater than 99%. • The proposed energy management system framework is conducive to green production. [ABSTRACT FROM AUTHOR]

Forecasting accuracy electricity load can help industrial enterprises optimise production scheduling based on peak and off-peak electricity prices. The electricity load forecasting results can be provided to an electricity system to improve electricity generation efficiency and minimize energy consumption by developing electricity generation plans in advance and by avoiding over or under the generation of electricity. However, because of the different informatization levels in different industries, few reliable intelligent electricity management systems are applied on the power supply side. Based on industrial big data and machine learning algorithms, this study proposes an integrated model to forecast short-term electricity load. The hybrid model based on the hybrid mode decomposition algorithms is proposed to decompose the total electricity load signal. To improve the generalisation ability of the forecasting model, a dynamic forecasting model is proposed based on the improved hybrid intelligent algorithm to forecast the short-term electricity load. The results show that the accuracy of the proposed dynamic integrated electricity load forecasting model is as high as 99%. The integrated framework could forecast abnormal electricity consumption in time and provide reliable evidence for production process scheduling. • Forecasting models are too difficult to embedded in industrial process systems. • The novel integrated framework has been proposed for industrial processes. • The proposed integrated model has been implemented in papermaking enterprise. • The accuracy of the proposed dynamic load forecasting model is greater than 99%. • The proposed energy management system framework is conducive to green production. [ABSTRACT FROM AUTHOR]

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]

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]

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]

Pulp and paper industry is one of the eight critical industries for controlling carbon emissions in China. As the paper productions increase, the pulp and paper industry may fail in achieving the emission reduction target due to the rapid growth of greenhouse gas emissions. The study uses life cycle assessment to evaluate the greenhouse gas emissions of China's papermaking industry chain in order to propose emission reduction targets and find ways to achieve the emission reduction targets for China's papermaking industry. Considering the net carbon absorption of plant raw materials and the extensive paper industrial chain, this study obtained that the greenhouse gas emission of major paper types under corresponding pathways ranges from 1.96 t CO 2 eq/t paper to 6.55 t CO 2 eq/t paper. Plant carbon sink can reduce greenhouse gas emissions by 14.3%–42.9% under virgin fiber-based pathways. This study found that the greenhouse gas emissions caused by terminal products of 814 papermaking enterprises counted in China were 282.23 Mt CO 2 eq in 2015, accounting for 10.23% of the total greenhouse gas emissions of manufacturing industries and construction. To achieve the emission reduction target by 2050, emissions of the pulp and paper industry in China have been investigated from the perspective of three ideal energy structures. The research findings show that the emission reduction target of Intended Nationally Determined Contribution will be achieved in 30 years if the energy structure of the pulp and paper industry is adjusted only based on the optimal 450 scenario. In terms of other energy structures, the pulp and paper industry should assume afforestation areas ranged from 5900 km2 to 223,000 km2 at least to make up for the greenhouse gas emissions beyond allowance, which accounts for 0.85%–32.16% of the existing areas of plantation forestry in China. • Statistics in this work reveal the problem of excess capacity in China's pulp and paper industry. • GHG emission of paper products in different pathways range from 1.95 to 6.52 t CO 2 eq/t paper. • Energy supply mode transition is conductive to green development of pulp and paper industry. • Pulp and paper enterprises should assume afforestation areas to achieve emission reduction targets. [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 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]

With the surging energy price and environmental concerns, measures to improve energy efficiency have attracted increasing concerns of the manufacture sector, especially energy-intensive manufacturing industries such as tissue paper mills. Energy-efficiency scheduling, as a novel energy-efficient method, has attracted the attention of an increasing number of researchers in recent years. Drying process is the most energy-intensive production process in tissue paper mills, which has a great energy-saving potential. This paper aims to reduce the energy costs for the tissue paper mill, consisting of processing energy cost and set-up energy cost, through integrating drying process optimization with energy-efficient scheduling. First, the energy cost model and the scheduling model were built. Then, the energy cost of the drying process of every job in a given scheduling problem was optimized using particle swarm optimization (PSO). Afterwards, the energy cost was further optimized using energy-efficiency scheduling. In addition, a hybrid non-dominated sorting genetic algorithm II (NSGA-II) was utilized to solve the energy-efficiency scheduling problem. Finally, several real scheduling problems from a real tissue paper mill were addressed using the proposed approach to demonstrate its effectiveness in energy saving. The experiment result showed that there is a great energy-saving potential in the drying process, accounting for up to 12.53% of the total energy consumption. Moreover, the maximum energy saving ratio of the proposed approach could reach 9.03%. On the whole, the proposed approach can provide a new energy-saving method for tissue paper mills or other manufacturing industries. [ABSTRACT FROM AUTHOR]

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]

Papermaking process is highly energy-intensive, complicated, and influenced by multiple unit processes, which plays an important role in the energy consumption, especially of thermal energy and electricity energy. An energy management system (EMS) was built to acquire the systematic, consistent, and accurate online information of the production process in a typical coated paperboard mill. The EMS first fulfilled the online energy information calculation from the process data acquisition and integration, second provided the massive historical data to find out the internal variation of production process for inferring decisions. In a case study, the online energy flow analysis and the estimation of specific energy consumption (SEC) were carried out. Based on data-driven techniques, three different operation conditions were recognized by adopting the kernel principal component analysis and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) when the basis weight was 220 g/m2. The energy consumption was described in each cluster. Particular attention needed to be paid to LP steam in the high-speed production process with the change in environmental temperature and humidity in cluster 3. A novel indicator was proposed to appraise the level of energy consumption in different paper machine speeds. The benchmarking SEC was extracted from the massive historical data for the estimation of energy-saving potential. With the designed capacity of 300,000 ton/year, the potential economic benefits will be 20127 thousand RMB/year. [ABSTRACT FROM AUTHOR]

The papermaking industry has developed rapidly in recent years in China. Papermaking is a high-energy-consuming process. Consequently, large-scale paper mills usually have cogeneration systems that supply both electricity and steam for the papermaking process. In China, almost all these cogeneration systems in paper mills are powered by coal combustion, which consumes a large amount of energy and emits large amounts of greenhouse gas. The Integrated Gasification Combined Cycle (IGCC) technology is regarded as a clean and efficient method of coal utilization. In this work, an IGCC process for application in paper mills was designed and modeled. An IGCC coupled with a cascade refrigeration process (CRP) was further proposed in order to reuse the waste heat from the dryer section and improve the energy efficiency of the papermaking process. A paper with a conventional cogeneration system, one with an IGCC cogeneration system, and one with an IGCC coupled with a CRP cogeneration system were analyzed and compared in terms of energy efficiency, capital investment, operational cost, and dynamic payback period. The results showed that the integration of IGCC technology allows paper mills to be more competitive than those featuring conventional cogeneration systems in terms of energy efficiency and operational cost. [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]

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]

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]

• A mathematical model is proposed for tissue paper drying process. • The model realizes the accurate simulation of key parameters in the drying process. • The model provides global optimization scheme for key operating parameters. • The model improves the competitiveness by reducing 8.72% of the drying cost. The drying process accounts for the largest proportion of energy consumption in paper mills. Energy system optimization has a great significance for reducing the energy consumption of the paper drying process. The drying process is a complex system that consists of several subsystems, such as cylinder and air hood systems. Previous optimization models for energy systems usually focused on these subsystems. A global optimization methodology for the entire drying process is lacking, and no existing models can be applied in practice. In this work, an energy system optimization model for the tissue paper drying process is proposed based on a process simulation model. The modeling process integrates the various subsystems and fully considers the coupling of the paper drying process, which greatly enhances the industrial application value of the model. Industrial operating data are used to test the simulation model, and the results show that the simulation error of each key variable is within 5%, which meets the real-world production requirements and lays the foundation for an energy efficiency analysis of each subsystem. Applying the optimization model to a tissue paper mill, the results show that it can reduce drying costs by 8.71%. [ABSTRACT FROM AUTHOR]

Abstract The paper drying process has the highest level of energy consumption in the pulp and paper production process. Analysis and optimization of the energy system during the paper drying process is critical for improving the energy efficiency of the entire paper mill. In the existing model for the paper drying process, the solution requires accurate boundary conditions such as the air temperature and humidity of the pocket area and the cylinder surface temperature, which are very difficult to obtain in the papermaking process. This can result in significant deviations between the model solution and the actual production process. This paper focuses on the single-tier dryer cylinder-based paper drying process that has been widely used with high-speed papermaking machines in recent years. A mathematical model is proposed based on real-time data. The verification via industrial production demonstrates that the proposed model is reliable for the paper drying process. Based on the simulation results, two optimization operations have been proposed. The energy consumption decreases from 1.51 t steam/t paper to 1.44 t steam/t paper, 4.6% of the steam and 1.26 × 106 RMB can be saved for a medium-scale paper mill with the annual production capacity of 105 t paper. Highlights • Mathematical model for the single-tier cylinder based paper drying process is proposed. • Both the steam-condensate system and the paper drying process are considered. • The simulation results are verified by the industrial data from a papermaking enterprise. [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]

Pulp making is a high-energy-consumption process that obtains its energy by directly combusting coal and black liquor in boiler, which is low efficiency and high emission. Mini-sulfide sulfite anthraquinone (MSSAQ) pulp making technology can reach 55%–66% pulping yield but suffers from bad economic performance. The combination of black liquor gasification (BLG) and chemical cycle combustion (CLC) can achieve high pulp production and clean production and has better economic performance. This research proposes an integrated CLC-BLG-MSSAQ pulp making process. Compared with conventional Kraft and MSSAQ processes, the CLC-BLG-MSSAQ process improves the energy efficiency by 6.2% and 12.2%, decreases carbon emissions by 0.62 t/ADt and 0.17 t/ADt. The economic analysis shows that under the scenario of USD 20/t CO 2 -eq or above, the CLC-BLG-MSSAQ process, which has higher combustion efficiency and lower carbon emission, will cost the least compared with Kraft and MSSAQ processes. • A MSSAQ pulp making process integrated with chemical looping combustion is developed. • The optimal operation condition of the proposed process is investigated. • Energy efficiency is improved and carbon emission is reduced by integrating CLC with MSSAQ. • Both economic and environmental insights of proposal process are provided. [ABSTRACT FROM AUTHOR]