Article: Industrial chain technology paths and economic feasibility of agricultural waste resource utilisation Journal: International Journal of Environment and Pollution (IJEP) 2025 Vol.75 No.4 pp.361 - 387 Abstract: In response to the high cost and insufficient coordination of the industrial chain in the utilisation of agricultural waste, this paper constructs an optimisation scheme for the coordinated scheduling of biocatalysis and digital twins. By screening heat-resistant β-glucosidase mutants and establishing straw-enzyme adaptation rules, combined with microwave-ultrasonic pretreatment and near-infrared (NIR), the conversion efficiency is improved. The virtual industrial chain model integrates graph convolutional networks (GCN) to predict raw material fluctuations, and improves the non-dominated sorting genetic algorithm II (NSGA-II) algorithm to achieve Pareto optimality of transportation and equipment utilisation. Experiments show that the reducing sugar yield of the mutant enzyme system reaches 90.2% in 72 h; the path optimisation rate of the scheduling system exceeds 0.8 within 12 months, and the equipment idle loss is controlled at 21,000-23,000 US dollars, which significantly improves the efficiency of resource utilisation. Inderscience Publishers - linking academia, business and industry through research

Title: Industrial chain technology paths and economic feasibility of agricultural waste resource utilisation

Authors: Lei Xu; Cheng Cheng; Ya'nan Zhang; Yuanlin Wang; Menglin Ni

Addresses: School of Business Administration, Shandong Women's University, Jinan, 250300, Shandong, China ' School of Marxism, Shandong University of Chinese Traditional Medicine, Jinan, 250300, Shandong, China ' Graduate School, Central University of Finance and Economics, Haidian, 100081, Beijing, China ' Department of Auditing, Weifang University, Weifang, 261061, Shandong, China ' School of Business Administration, Shandong Women's University, Jinan, 250300, Shandong, China

Abstract: In response to the high cost and insufficient coordination of the industrial chain in the utilisation of agricultural waste, this paper constructs an optimisation scheme for the coordinated scheduling of biocatalysis and digital twins. By screening heat-resistant β-glucosidase mutants and establishing straw-enzyme adaptation rules, combined with microwave-ultrasonic pretreatment and near-infrared (NIR), the conversion efficiency is improved. The virtual industrial chain model integrates graph convolutional networks (GCN) to predict raw material fluctuations, and improves the non-dominated sorting genetic algorithm II (NSGA-II) algorithm to achieve Pareto optimality of transportation and equipment utilisation. Experiments show that the reducing sugar yield of the mutant enzyme system reaches 90.2% in 72 h; the path optimisation rate of the scheduling system exceeds 0.8 within 12 months, and the equipment idle loss is controlled at 21,000-23,000 US dollars, which significantly improves the efficiency of resource utilisation.

Keywords: agricultural waste; resource utilisation; industrial chain technology path; economic feasibility; digital twin model; GCN; graph convolutional networks; NSGA-II; non-dominated sorting genetic algorithm II.

DOI: 10.1504/IJEP.2025.150946

International Journal of Environment and Pollution, 2025 Vol.75 No.4, pp.361 - 387

Received: 14 Apr 2025
Accepted: 24 Oct 2025
Published online: 05 Jan 2026 *

Title: Industrial chain technology paths and economic feasibility of agricultural waste resource utilisation

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