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Elevated medical waste has urged the improvement of sustainable medical waste treatments. A bibliometric analysis is initially conducted to investigate scientific development of medical waste management to pinpoint the publication trends, influential articles, journals and countries and study hotspots. Publications on medical waste and its management sharply increased since 2020. The most influential article was written by Klemeš et al., and "Waste Management and Research" is the most productive journal. India, China, the United Kingdom, Iran and Italy have published the most works. The research spotlights have switched from "human" and "sustainable development" in 2019 to "COVID-19" and "circular economy" in 2021. Since government acts essentially in handling medical waste and controlling disease transmission, rule implementations among the abovementioned countries are summarized to seek gaps between scientific advancement and regulatory frameworks. For accomplishing a circular economy, waste-to-energy technologies (incineration, gasification, pyrolysis, plasma-based treatments, carbonization, hydrogenation, liquefaction, biomethanation, fermentation and esterification) are comprehensively reviewed. Incineration, gasification, pyrolysis and carbonization are relatively feasible methods, their characteristics and limitations are further compared. By holistically reviewing current status of medical waste research, the focal points involved in management at the policy and technical level have been highlighted to find proper routes for medical waste valorization. [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. • Incorporated ESG practice into process level for medical waste valorization alternatives. • Pioneered an ESG metrics framework for accessing long-term value of medical waste valorization alternative. • Proposed a novel integration model of DFS-WINGS and DFS-CODAS to evaluate ESG criteria and select sustainable alternative. • Supply chain traceability, Risk monitoring index, and Acidifying emissions – top three priority causal factors. • "Rotary kiln incineration with heat recovery and flue gas purification" – optimal medical waste valorization alternative. [ABSTRACT FROM AUTHOR]

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]

The COVID-19 pandemic has generated substantial medical waste (MW), posing risks to society. Based on widespread MW incineration, this study proposes an integrated system with tail gas treatment to convert MW into value-added products with nearly zero emissions. Herein, steam generators and supercritical CO 2 cycles were used to recover energy from MW to produce high-temperature/pressure steam and electricity. A simple power generation cycle achieved a net electricity efficiency of 22.4% through optimization. Thermodynamic analysis revealed that the most energy and exergy loss occurred in incineration. Furthermore, a pressurized reactive distillation column purified the resultant tail gas. The effects of inlet temperature, pressure, liquid/gas ratio, and recycle ratio on the removal and conversion efficiencies of NO 2 and SO 2 were evaluated. Nearly 100% of the SO 2 and 75% of the NO 2 generated by the incineration of MW have been converted into their acid forms. Based on the proposed tail gas treatment unit, high-purity CO 2 (∼98% purity) was finally obtained. [Display omitted] • An incineration-based process integrating waste-to-energy and tail gas treatment. • The most of energy and exergy loss has been identified by thermodynamic analysis. • 22.4% of net electricity efficiency has been achieved after optimization. • SO 2 and NO 2 generated in incineration can be converted into value-added byproducts. [ABSTRACT FROM AUTHOR]