Renewable energy technology is suitable for reducing energy consumption and emissions, and the corresponding impact on energy poverty has aroused tremendous attention. This paper proposed the best-worst method- (BWM-) based quality function deployment (QFD) approach within interval-valued intuitionistic fuzzy number (IVIFN) to select the appropriate renewable energy technology for energy poverty alleviation. QFD is firstly used to explore the relationship between energy poverty reduction requirements (CRs), renewable energy technology selection criteria (TRs), and correlation among TRs. Interval-valued intuitionistic fuzzy (IVIF) BWM is then applied for obtaining the correlation among CRs. After that, the IVIF-QFD method is used to attain the weight of TRs, which are then used to evaluate the renewable energy technology alternatives through IVIF-VIKOR approach. The six representative renewable energy technologies, including wind energy, solar energy, biomass (direct combustion, combined heat and power, and gasification), and hydropower have been selected in the decision model, and the result shows that the large-scale hydropower could be selected as the best choice to reduce the energy poverty issues, whose interval numbers is [0, 0.2925]. Except for prioritization of the selected technologies, findings of this paper could also contribute to developing sustainable renewable energy policies and energy roadmaps.

Modeling and optimization is crucial to smart chemical process operations. However, a large number of nonlinearities must be considered in a typical chemical process according to complex unit operations, chemical reactions and separations. This leads to a great challenge of implementing mechanistic models into industrial-scale problems due to the resulting computational complexity. Thus, this paper presents an efficient hybrid framework of integrating machine learning and particle swarm optimization to overcome the aforementioned difficulties. An industrial propane dehydrogenation process was carried out to demonstrate the validity and efficiency of our method. Firstly, a data set was generated based on process mechanistic simulation validated by industrial data, which provides sufficient and reasonable samples for model training and testing. Secondly, four well-known machine learning methods, namely, K-nearest neighbors, decision tree, support vector machine, and artificial neural network, were compared and used to obtain the prediction models of the processes operation. All of these methods achieved highly accurate model by adjusting model parameters on the basis of high-coverage data and properly features. Finally, optimal process operations were obtained by using the particle swarm optimization approach.

We reveal the origins of surface groups on pristine and defective graphene after oxidation with potassium permanganate in aqueous solution. Density functional theory calculations show that the hydroxyl group that is first introduced onto graphene via hydrolysis of permanganate ester plays an important role in the oxidative cutting of graphene. Our results demonstrate that oxidative unzipping of defect-free graphene will occur from the edge and the inner plane simultaneously in terms of comparable maximum barrier heights along these two reaction routes. Divacancy on the defective graphene may accelerate the etching process when the hydroxyl group is introduced at the defect edge. Different from the previous unzipping mechanisms producing graphene flakes with only zigzag edges, our new mechanism involves hydroxyl and both sides of the graphene sheet, which allows richer edge states after oxidative cutting that agree with experimental observations. The detailed molecular insight into the mechanisms for graphene oxidation and fragmentation will be valuable for developing an effective means for graphene manipulation and interpretation of the long-puzzling graphene oxide structure.

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.

Volatile fatty acids (VFAs) and methane are the main products of rumen fermentation. Quantitative studies of rumen fermentation parameters can be performed using in vitro techniques and machine learning methods. The currently proposed models suffer from poor generalization ability due to the small number of samples. In this study, a prediction model for rumen fermentation parameters (methane, acetic acid (AA), and propionic acid (PA)) of dairy cows is established using the stacking ensemble learning method and in vitro techniques. Four factors related to the nutrient level of total mixed rations (TMRs) are selected as inputs to the model: neutral detergent fiber (NDF), acid detergent fiber (ADF), crude protein (CP), and dry matter (DM). The comparison of the prediction results of the stacking model and base learners shows that the stacking ensemble learning method has better prediction results for rumen methane (coefficient of determination (R2) = 0.928, root mean square error (RMSE) = 0.968 mL/g), AA (R2 = 0.888, RMSE = 1.975 mmol/L) and PA (R2 = 0.924, RMSE = 0.74 mmol/L). And the stacking model simulates the variation of methane and VFAs in relation to the dietary fiber content. To demonstrate the robustness of the model in the case of small samples, an independent validation experiment was conducted. The stacking model successfully simulated the transition of rumen fermentation type and the change of methane content under different concentrate-to-forage (C:F) ratios of TMR. These results suggest that the rumen fermentation parameter prediction model can be used as a decision-making basis for the optimization of dairy cow diet compositions, rapid screening of methane emission reduction, feed beneficial to dairy cow health, and improvement of feed utilization.