Analysis of energy saving and emission reduction of secondary fiber mill based on data mining

Waste paper recycling is an important way to realize the environmental protection development of the papermaking industry. The quality of the pulp will affect the pulp sales of the secondary fiber paper mills. The waste paper pulp can be adjusted by controlling the pulping process working conditions, but the working conditions of the waste paper pulping process have too many parameters. And the parameters are coupled with each other, it is difficult to control. In order to find the best working conditions and improve the quality of the pulp, this study uses the association rules algorithm to optimize the parameters for the waste paper pulping process. These parameters are power of refiner, waste paper concentration of refiner, the volume of slurry that enters deinked process, deinking agent amount, deinking time, deinking temperature, bleaching agent amount, bleaching time, and bleaching temperature. The test results show that the qualified rate of the pulp produced under the improved working conditions is 92.56%, an increase of 6.93%, and the average electricity consumption per ton of pulp is reduced by 5.76 kWh/t. In addition to potential economic benefits, this method can reduce carbon emissions

Monitoring Water and Energy Cycles at Climate Scale in the Third Pole Environment (CLIMATE-TPE)

A better understanding of the water and energy cycles at climate scale in the Third Pole Environment is essential for assessing and understanding the causes of changes in the cryosphere and hydrosphere in relation to changes of plateau atmosphere in the Asian monsoon system and for predicting the possible changes in water resources in South and East Asia. This paper reports the following results: (1) A platform of in situ observation stations is briefly described for quantifying the interactions in hydrosphere-pedosphere-atmosphere-cryosphere-biosphere over the Tibetan Plateau. (2) A multiyear in situ L-Band microwave radiometry of land surface processes is used to develop a new microwave radiative transfer modeling system. This new system improves the modeling of brightness temperature in both horizontal and vertical polarization. (3) A multiyear (2001–2018) monthly terrestrial actual evapotranspiration and its spatial distribution on the Tibetan Plateau is generated using the surface energy balance system (SEBS) forced by a combination of meteorological and satellite data. (4) A comparison of four large scale soil moisture products to in situ measurements is presented. (5) The trajectory of water vapor transport in the canyon area of Southeast Tibet in different seasons is analyzed, and (6) the vertical water vapor exchange between the upper troposphere and the lower stratosphere in different seasons is presented

Machine Learning-Based Energy System Model for Tissue Paper Machines

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

Machine Learning Based Optimization Model for Energy Management of Energy Storage System for Large Industrial Park

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

Cost Optimal Production-Scheduling Model Based on VNS-NSGA-II Hybrid Algorithm—Study on Tissue Paper Mill

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

Engineering UiO-68-Typed Homochiral Metal–Organic Frameworks for the Enantiomeric Separation of Fmoc-AAs and Mechanism Study

Homochiral metal–organic frameworks (HMOFs) have been widely investigated in the application of enantiomeric separation. Nonetheless, it remains a significant challenge to explore the effect of multiple weak interactions between HMOF adsorbents and chiral adsorbates on enantiomeric separation performance still. In this work, robust chiral amine-alcohol-functionalized UiO-68-typed Zr-HMOFs 1–3 with the same hydrogen-bonding sites but slightly different π-binding sites were prepared for the enantioseparation of amino acid derivatives (Fmoc-AAs) with large π-binding groups. As a consequence of multiple host–guest interactions, these Zr-HMOFs exhibit speedy adsorption and high adsorption capacity for Fmoc-L/D-AAs and dissimilar enantioselectivity for the adsorption of their enantiomers. Materials 1 and 2 exhibit excellent enantioselective separation performance for Fmoc-valine with a single terminal π-binding group, while material 3 displays excellent enantioselective separation performance for Fmoc-phenylalanine and Fmoc-tryptophan with π-binding groups at both ends. As evidently demonstrated by our experimental and density functional theory (DFT) computational results, when the number of π-binding groups preset in the confined chiral space of adsorbents matches the number of π-binding groups of chiral adsorbates, the synergism of π–π or σ–π interactions will increase enantioselectivity; otherwise, the competition interactions from redundant identical binding sites will weaken enantioselectivity. Our case not only provides a tremendously typical system for investigating the collaborative discrimination of multiple weak interactions and exploring the impact of relatively excessive binding sites of HMOF adsorbents or chiral adsorbates on the enantioselective separation performance but also provides guidance for targeted functional modifications of high-performance chiral porous materials

Benchmarking Analysis of Energy Efficiency Indicators in Paper Mill

Paper mills consume a large amount of energy, which is an important factor restricting their sustainable development. Benchmarking is a critical method for discovering the energy-savings potential of mills. To address problems such as the absence of indicators for energy efficiency benchmarking, the influence of different basis weights on energy efficiency levels and on the estimation of energy-saving potential, this paper makes use of production line-based and process-based benchmarking in coated paperboard production. The indicator system is constructed to collect data and quantify the energy efficiency. K-means clustering is used to classify the basis weight and energy efficiency data for seven months and obtain the benchmark values. The results showed that the specific energy consumption (SEC) decreased with the increase in basis weight. An analysis of production line-based benchmarking for a paper mill in China indicated that energy efficiency reached the level of 5.92 to 6.94 GJ/t, which was 10.8 to 23.91% lower compared with the European Union best available energy level (7.78 GJ/t) and 6.28 to 24.6% higher compared with the energy consumption of American paper products integrated production units (5.57 GJ/t). These energy-saving measures should be taken into account in order to raise the energy efficiency in paper mills

Engineering UiO-68-Typed Homochiral Metal–Organic Frameworks for the Enantiomeric Separation of Fmoc-AAs and Mechanism Study

Homochiral metal–organic frameworks (HMOFs) have been widely investigated in the application of enantiomeric separation. Nonetheless, it remains a significant challenge to explore the effect of multiple weak interactions between HMOF adsorbents and chiral adsorbates on enantiomeric separation performance still. In this work, robust chiral amine-alcohol-functionalized UiO-68-typed Zr-HMOFs 1–3 with the same hydrogen-bonding sites but slightly different π-binding sites were prepared for the enantioseparation of amino acid derivatives (Fmoc-AAs) with large π-binding groups. As a consequence of multiple host–guest interactions, these Zr-HMOFs exhibit speedy adsorption and high adsorption capacity for Fmoc-L/D-AAs and dissimilar enantioselectivity for the adsorption of their enantiomers. Materials 1 and 2 exhibit excellent enantioselective separation performance for Fmoc-valine with a single terminal π-binding group, while material 3 displays excellent enantioselective separation performance for Fmoc-phenylalanine and Fmoc-tryptophan with π-binding groups at both ends. As evidently demonstrated by our experimental and density functional theory (DFT) computational results, when the number of π-binding groups preset in the confined chiral space of adsorbents matches the number of π-binding groups of chiral adsorbates, the synergism of π–π or σ–π interactions will increase enantioselectivity; otherwise, the competition interactions from redundant identical binding sites will weaken enantioselectivity. Our case not only provides a tremendously typical system for investigating the collaborative discrimination of multiple weak interactions and exploring the impact of relatively excessive binding sites of HMOF adsorbents or chiral adsorbates on the enantioselective separation performance but also provides guidance for targeted functional modifications of high-performance chiral porous materials

Engineering UiO-68-Typed Homochiral Metal–Organic Frameworks for the Enantiomeric Separation of Fmoc-AAs and Mechanism Study

Homochiral metal–organic frameworks (HMOFs) have been widely investigated in the application of enantiomeric separation. Nonetheless, it remains a significant challenge to explore the effect of multiple weak interactions between HMOF adsorbents and chiral adsorbates on enantiomeric separation performance still. In this work, robust chiral amine-alcohol-functionalized UiO-68-typed Zr-HMOFs 1–3 with the same hydrogen-bonding sites but slightly different π-binding sites were prepared for the enantioseparation of amino acid derivatives (Fmoc-AAs) with large π-binding groups. As a consequence of multiple host–guest interactions, these Zr-HMOFs exhibit speedy adsorption and high adsorption capacity for Fmoc-L/D-AAs and dissimilar enantioselectivity for the adsorption of their enantiomers. Materials 1 and 2 exhibit excellent enantioselective separation performance for Fmoc-valine with a single terminal π-binding group, while material 3 displays excellent enantioselective separation performance for Fmoc-phenylalanine and Fmoc-tryptophan with π-binding groups at both ends. As evidently demonstrated by our experimental and density functional theory (DFT) computational results, when the number of π-binding groups preset in the confined chiral space of adsorbents matches the number of π-binding groups of chiral adsorbates, the synergism of π–π or σ–π interactions will increase enantioselectivity; otherwise, the competition interactions from redundant identical binding sites will weaken enantioselectivity. Our case not only provides a tremendously typical system for investigating the collaborative discrimination of multiple weak interactions and exploring the impact of relatively excessive binding sites of HMOF adsorbents or chiral adsorbates on the enantioselective separation performance but also provides guidance for targeted functional modifications of high-performance chiral porous materials

The Immune Subtypes and Landscape of Advanced-Stage Ovarian Cancer

Immunotherapy has played a significant role in the treatment of a variety of hematological and solid tumors, but its application in ovarian cancer (OC) remains unclear. This study aimed to identify immune subtypes of OC and delineate an immune landscape for selecting suitable patients for immunotherapy, thereby providing potent therapeutic targets for immunotherapy drug development. Three immune subtypes (IS1–IS3) with distinctive molecular, cellular, and clinical characteristics were identified from the TCGA and GSE32062 cohorts. Compared to IS1, IS3 has a better prognosis and exhibits an immunological “hot”. IS3, in contrast, exhibits an immunological “cold” and has a worse prognosis in OC patients. Moreover, gene mutations, immune modulators, CA125, CA199, and HE4 expression, along with sensitivity either to immunotherapy or chemotherapy, were significantly different among the three immune subtypes. The OC immune landscape was highly heterogeneous between individual patients. Poor prognosis was correlated with low expression of the hub genes CD2, CD3D, and CD3E, which could act not only as biomarkers for predicting prognosis, but also as potential immunotherapy targets. Our study elucidates the immunotyping and molecular characteristics of the immune microenvironment in OC, which could provide an effective immunotherapy stratification method for optimally selecting patients, and also has clinical significance for the development of new immunotherapy as well as rational combination strategies for the treatment of OC patients