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A preliminary bottom-up analysis of the energy use in the chemical industry has been performed, using a model containing datasets on production processes for 52 of the most important bulk chemicals as well as production volumes for these chemicals. The processes analysed are shown to cover between 70% and 100% of the total energy use in the chemical sector. Energy use and the heat effects of the reactions taking place are separately quantified. The processes are also compared with energetically-ideal processes following the stoichometric reactions. The comparison shows that there is significant room for process improvements, both in the direction of more selective processes and in the direction of further energy-savings.

A preliminary bottom-up analysis of the energy use in the chemical industry has been performed, using a model containing datasets on production processes for 52 of the most important bulk chemicals as well as production volumes for these chemicals. The processes analysed are shown to cover between 70% and 100% of the total energy use in the chemical sector. Energy use and the heat effects of the reactions taking place are separately quantified. The processes are also compared with energetically-ideal processes following the stoichometric reactions. The comparison shows that there is significant room for process improvements, both in the direction of more selective processes and in the direction of further energy-savings.

Applied Energy, 258 ISSN:0306-2619 ISSN:1872-9118

Applied Energy, 258 ISSN:0306-2619 ISSN:1872-9118

Energy-intensive manufacturing industries are characterised by high pollution and heavy energy consumption, severely challenging the ecological environment. Fortunately, environmental, social, and governance (ESG) can promote energy-intensive manufacturing enterprises to achieve smart and sustainable production. In Industry 4.0, various advanced technologies are used to achieve smart manufacturing, but the sustainability of production is often ignored without considering ESG performance. This study proposes a strategy of edge-cloud cooperation -driven smart and sustainable production to realise data collection, preprocessing, storage and analysis. In detail, kernel principal component analysis (KPCA) is used to decrease the interference of abnormal data in the eval-uation results. Subsequently, an improved technique for order preference by similarity to ideal solution (TOPSIS) based on the adversarial interpretative structural model (AISM) is proposed to evaluate the production efficiency of the manufacturing workshop and make the analysis results more intuitive. Then, the architecture and models are verified using real production data from a partner company. Finally, sustainable analysis is discussed from the perspective of energy consumption, economic impact, greenhouse gas emissions and pollution prevention. Funding Agencies|Youth Innovation Team of Shaanxi Universities ?; Special ConstructionFund for Key Disciplines of Shaanxi Provincial Higher Education; Natural Science Basic Research Plan in Shaanxi Province of China [2022JQ-37]; Shaanxi Provincial Education Department [22JK0567]; Project of National Natural Science Foundation of China [62271390, 51905399]; Postgraduate Innovation Fund of Xian University of Posts and Telecommunications [CXJJDL2022012]

Energy-intensive manufacturing industries are characterised by high pollution and heavy energy consumption, severely challenging the ecological environment. Fortunately, environmental, social, and governance (ESG) can promote energy-intensive manufacturing enterprises to achieve smart and sustainable production. In Industry 4.0, various advanced technologies are used to achieve smart manufacturing, but the sustainability of production is often ignored without considering ESG performance. This study proposes a strategy of edge-cloud cooperation -driven smart and sustainable production to realise data collection, preprocessing, storage and analysis. In detail, kernel principal component analysis (KPCA) is used to decrease the interference of abnormal data in the eval-uation results. Subsequently, an improved technique for order preference by similarity to ideal solution (TOPSIS) based on the adversarial interpretative structural model (AISM) is proposed to evaluate the production efficiency of the manufacturing workshop and make the analysis results more intuitive. Then, the architecture and models are verified using real production data from a partner company. Finally, sustainable analysis is discussed from the perspective of energy consumption, economic impact, greenhouse gas emissions and pollution prevention. Funding Agencies|Youth Innovation Team of Shaanxi Universities ?; Special ConstructionFund for Key Disciplines of Shaanxi Provincial Higher Education; Natural Science Basic Research Plan in Shaanxi Province of China [2022JQ-37]; Shaanxi Provincial Education Department [22JK0567]; Project of National Natural Science Foundation of China [62271390, 51905399]; Postgraduate Innovation Fund of Xian University of Posts and Telecommunications [CXJJDL2022012]

Abstract Thermochemical biomass-to-fuel conversion requires an increased hydrogen concentration in the syngas derived from gasification, which is currently achieved by water–gas-shift reaction and CO2 removal. State-of-the-art biomass-to-fuels convert less than half of the biomass carbon with the remaining emitted as CO2. Full conversion of biomass carbon can be achieved by integrating solid-oxide electrolyzer with different concepts: (1) steam electrolysis with the hydrogen produced injected into syngas, and (2) co-electrolysis of CO2 and H2O to convert the CO2 captured from the syngas. This paper investigates techno-economically steam- or co-electrolysis-based biomass-to-fuel processes for producing synthetic natural gas, methanol, dimethyl ether and jet fuel, considering system-level heat integration and optimal placement of steam cycles for heat recovery. The results show that state-of-the-art biomass-to-fuels achieve similar energy efficiencies of 48–51% (based on a lower heating value) for the four different fuels. The integrated concept with steam electrolysis achieves the highest energy efficiency: 68% for synthetic natural gas, 64% for methanol, 63% for dimethyl ether, and 56% for jet fuel. The integrated concept with co-electrolysis can enhance the state-of-the-art energy efficiency to 66% for synthetic natural gas, 61% for methanol, and 54% for jet fuel. The biomass-to-dimethyl ether with co-electrolysis only reaches an efficiency of 49%, due to additional heat demand. The levelized cost of the product of the integrated concepts highly depends on the price and availability of renewable electricity. The concept with co-electrolysis allows for additional operation flexibility without renewable electricity, resulting in high annual production. Thus, with limited annual available hours of renewable electricity, biomass-to-fuel with co-electrolysis is more economically convenient than that with steam electrolysis. For a plant scale of 60 MWth biomass input with the renewable electricity available for 1800 h annually, the levelized cost of product of biomass-to-synthesis-natural-gas with co-electrolysis is 35 $/GJ, 20% lower than that with steam-electrolysis.