Journal of Applied Science and Engineering

Published by Tamkang University Press

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Decarbonization Optimization of a Waste Heat Recovery System for Waste Tire Pyrolysis: Integrating AI-based Predictive Modeling and Thermodynamic Performance Validation

 2026

 Volume 33

Shih-Hsing Chang, Chin-Han Tsai, and Chyan-Chyi Wu

Department of Mechanical and Electromechanical Engineering, Tamkang University, Taiwan

Received: April 16, 2026
Accepted: June 26, 2026
Publication Date: July 25, 2026

上傳圖片

ORC Experimental Setup (with continuous pipeline layout and sensor  configuration). 

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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20%–30% of total energy consumption in the global industrial sector is lost in the form of low-grade waste heat. The 100–200 ◦C exhaust waste heat generated by the pyrolysis process of waste tires has a remarkably high recovery potential, but its organic Rankine cycle (ORC) system has difficulty in optimizing efficiency due to multi-parameter nonlinear coupling. This study proposes a hybrid optimization framework that integrates a fractional factorial Taguchi Design of Experiments (DOE), a two-layer Stacking Ensemble Learning architecture (SVR+ANN), and a Genetic Algorithm (GA), aiming to solve the inefficiency and poor local convergence of traditional trial-and-error or isolated optimization methods in engineering practice. The results show that by systematically regulating the evaporation temperature, condensation temperature, working fluid mass flow rate, and expansion ratio, the thermal efficiency of the ORC system has significantly improved from the baseline value of 13.2% to 26.9%. In addition, a comprehensive environmental assessment framework with strict system boundaries was introduced. Considering auxiliary energy consumption and working fluid global warming potential (GWP), the evaluation showed that the optimized system could reduce net emissions by 92.5 kg for every ton of waste tires processed based on localized grid emission factors. Experimental verification, backed by an independent validation dataset and uncertainty analysis, shows that the prediction deviation is less than 0.2%, confirming the excellent generalization capability, robustness, and reliability of this framework in industrial low-carbon transformation practice.

Keywords: Organic Rankine Cycle (ORC), Waste tire pyrolysis, Ensemble learning (Stacking), Genetic algorithm (GA), Carbon reduction, Engineering optimization

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