Fuente: Sustainability - Revista científica (MDPI) Sustainability, Vol. 18, Pages 2616: Enhancing Sustainable Waste-to-Energy: A Multi-Controlled Variable Prediction Model for Municipal Solid Waste Incineration Using Shared Features and an Improved Fuzzy Neural Network
Sustainability doi: 10.3390/su18052616
Authors:
Qiumei Cong
Lu
Tang

Municipal solid waste incineration (MSWI) is a critical technology for advancing urban sustainability, contributing to improved environmental quality, optimized energy structures, and the circular economy. However, the realization of these sustainability benefits is contingent upon the stable, efficient, and low-emission operation of the incineration process. This operational stability is directly governed by several key variables, such as furnace temperature, main steam flow rate, flue gas oxygen content, and burnout point temperature. The inherent complexity of controlling these interconnected variables necessitates the development of an accurate multi-variable prediction model to ensure both energy recovery efficiency and environmental compliance, which are core pillars of sustainable waste management. Existing studies have often addressed these key controlled variables in isolation, lacking a unified modeling framework. Furthermore, they have not adequately considered how dimensional differences among these variables impact the performance evaluation of predictive models, a critical oversight for ensuring holistic process sustainability. To address these gaps and support the intelligent operation of sustainable waste-to-energy systems, this study proposes a novel multi-controlled variable modeling method based on shared features and an improved fuzzy neural network. Our integrated approach begins by calculating the Pearson correlation coefficient between each manipulated variable and each controlled variable—selected based on expert knowledge—to assess the distinguishability of operating conditions within the current dataset. Subsequently, a correlation threshold, informed by expert knowledge, is applied to identify shared features that influence multiple controlled variables simultaneously. Finally, we enhance the fuzzy neural network by redefining its evaluation criterion to accommodate variable dimensional differences, leading to the development of a robust multi-controlled variable prediction model. This model is designed to provide a more comprehensive and accurate basis for process control, directly contributing to improved energy efficiency and reduced environmental impact. The effectiveness of our proposed model is validated using operational data from an actual MSWI plant, demonstrating its potential to support more sustainable and economically viable waste-to-energy operations.