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Operations optimization in an organic Rankine cycle (ORC) based combined system is important while computationally difficult by using mechanistic models due to complex nonlinearities and constraints. In this study, a hybrid framework integrating machine learning and mathematical programming has been proposed to optimize the operations of the system for the best exergy performance. The combined system is first decomposed into two single ORCs for reducing computational complexity. Classification models and regression models based on artificial neural network (ANN) and linear regression are developed using simulation data, where classifications can be employed for high-throughput screening feasible inputs which meet the mechanistic constraints in ORC. The results demonstrate high performances of machine learning with at least 99% accuracies for classifications and with mean relative errors of less than 1% for regressions. These data-driven models and the relation of two ORCs were then embedded with mathematical programming for optimization and maximum net exergy of 28.66 MW is obtained. By linear expansion of ReLU operators in ANN, mixed-integer linear programming (MILP) based on machine learning models achieve high efficiency with ∼0.1 s required for optimization compared to mixed-integer nonlinear programming (MINLP) (>1000 s) and heuristic optimization based on mechanistic models (>10 h). • Machine learning and math programming are integrated for ORC optimization. • Classification is used for high-throughput identifying the mechanistic constraints. • Machine learning achieve >99% accuracy and < 1% mean relative error. • ReLU activation function is expressed by linear equations. • The method's efficiency is validated by optimization of ORC-based combined system. [ABSTRACT FROM AUTHOR]

As global crises escalate, the spotlight on sustainable development and Environmental, Social, and Governance (ESG) concerns intensifies. The amount of medical waste has reached an unprecedented level, and achieving a net-zero emissions target underlines the need for efficient waste management strategies. Despite various proposed valorization processes, there remains a critical need for an integrated assessment framework capable of evaluating their performance, efficiency, sustainability, and efficacy. This study proposed an innovative ESG metrics framework considering sixteen attributes for evaluating medical waste valorization alternatives, which aims to comprehensively address the multi-criteria nature of medical waste management, incorporating factors such as greenhouse gas emissions, energy efficiency, and more. In order to accommodate the varying perspectives of decision-makers ranging from optimistic to pessimistic, decomposed fuzzy sets (DFS) are utilized to address the inherent uncertainties in the assessment process. Integrating the Weighted Influence Nonlinear Gauge System (WINGS) and Combinative Distance-based Assessment (CODAS) under a DFS environment can determine criteria prominence, cause-effect relationships, and alternative rankings. More specifically, five alternatives for medical waste valorization: rotary kiln incineration, pyrolysis incineration, steam gasification, and plasma gasification with different sub-systems are considered in this study. The results indicate that eight criteria were reflected as causal criteria that directly influence process performance, and improving them may directly influence the remaining eight effectual criteria. Among five valorization alternatives, 'rotary kiln incineration with heat recovery and flue gas purification' is identified as the optimal solution under the ESG metrics. The proposed model's robustness and viability are validated through sensitivity analysis, comparative assessment, and correlation investigation with other DFS-based models. Finally, theoretical and policy implications are drawn to provide valuable insights for policymakers and other stakeholders in medical waste valorization., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)