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Simple Summary: Our objective is to establish an in vitro rumen fermentation model that can dynamically simulate the fermentation process of various total mixed ration (TMR) diets in the rumen of dairy cows, enabling a quantitative investigation of rumen methane and rumen acetic acid concentrations. The models were assessed for their prediction accuracy and precision, while the independent verification experiments confirmed the models' generalization ability across different total mixed ration (TMR) ratios (C:F). These results show that in vitro rumen models constructed by machine learning methods can be used as a tool to quantify rumen fermentation parameters (methane and acetic acid) and guide the dietary structure optimization of dairy cows. The aim of this study is to identify an alternative approach for simulating the in vitro fermentation and quantifying the production of rumen methane and rumen acetic acid during the rumen fermentation process with different total mixed rations. In this experiment, dietary nutrient compositions (neutral detergent fiber (NDF), acid detergent fiber (ADF), crude protein (CP), and dry matter (DM)) were selected as input parameters to establish three prediction models for rumen fermentation parameters (methane and acetic acid): an artificial neural network model, a genetic algorithm-bp model, and a support vector machine model. The research findings show that the three models had similar simulation results that aligned with the measured data trends (R2 ≥ 0.83). Additionally, the root mean square errors (RMSEs) were ≤1.85 mL/g in the rumen methane model and ≤2.248 mmol/L in the rumen acetic acid model. Finally, this study also demonstrates the models' capacity for generalization through an independent verification experiment, as they effectively predicted outcomes even when significant trial factors were manipulated. These results suggest that machine learning-based in vitro rumen models can serve as a valuable tool for quantifying rumen fermentation parameters, guiding the optimization of dietary structures for dairy cows, rapidly screening methane-reducing feed options, and enhancing feeding efficiency. [ABSTRACT FROM AUTHOR]

Simple Summary: Methane and volatile fatty acids are important products of rumen fermentation in dairy cows. Quantitative research on them is of great significance for environmental protection and animal production. The aim of this study was to develop a prediction model using the stacking ensemble learning method and predict the production of rumen fermentation products based on the nutrient level of the diet. The results show that the stacking model has good performance in terms of prediction accuracy. The model proposed in this study can be used as a guideline to optimize diet compositions and improve feeding efficiency. 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 ( R 2 ) = 0.928, root mean square error (RMSE) = 0.968 mL/g), AA ( R 2 = 0.888, RMSE = 1.975 mmol/L) and PA ( R 2 = 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. [ABSTRACT FROM AUTHOR]

Biomass gasification process has been predicted and optimized based on process temperature, pressure, and gasifying agent ratios by integrating Aspen Plus simulation with the high-dimensional model representation (HDMR) method. Results show that temperature and biomass to air ratio (BMR) have significant effects on gasification process. HDMR models demonstrated high performance in predicting H2, net heat (NH), higher heating value (HHV), and lower heating value (LHV) with coefficients of determination 0.96, 0.97, 0.99, and 0.99, respectively. HDMR-based single-objective optimization has maximum outputs for H2, HHV, and LHV (0.369 of mole fractions, 340 kJ/mol, and 305 kJ/mol, respectively) but NH would be negative at these conditions, which indicates that process is not energy-efficient. The optimal solution was determined by the multiobjective which produced 0.24 mole fraction of H2, 158.17 kJ/mol of HHV, 142.48 kJ/mol of LHV, and 442.37 kJ/s NH at 765°C, 0.59 BMR, and 1 bar. Therefore, these parameters can provide an optimal solution for increasing gasification yield, keeping process energy-efficient. [ABSTRACT FROM AUTHOR]

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]

This study proposes a novel system designed to turn waste masks (WM) into hydrogen while aiming at carbon neutrality. The system utilizes plasma gasification to primarily convert WM into syngas, which is subsequently upgraded through water gas shift to enhance hydrogen fraction. To mitigate carbon emissions, carbon capture techniques including monoethanolamine-based carbon absorption and oxy-fuel combustion are incorporated into the system. A high-fidelity model of the designed process is developed in Aspen Plus. Machine learning-driven optimization is applied to identify the optimal operating conditions. Utilizing Gaussian process regression-based surrogate optimization, the system achieves around 0.5 kg CO 2 eq/kg H 2 of levelized emissions per unit hydrogen (LEH) in the reaction stage and the execution time required for optimization is only 4 s, outperforming detailed process models-based optimization. Energy assessment highlights plasma gasification and CO 2 absorption as the primary sources of energy loss. Furthermore, life cycle analysis indicates that CO 2 absorption is the primary contributor to carbon emissions, accounting for approximately 60% of the total emissions. With levelized emissions of 1.27 kg CO 2 eq/kg WM and 5.18 CO 2 eq/kg H 2 , the designed system demonstrates superiority over conventional medical waste treatment and hydrogen production methods. • A waste mask-to-H 2 system with carbon capture is designed and modeled. • Zero direct carbon emission is achieved. • GPR-based surrogate optimization demonstrates high efficiency. • The optimal system shows an emission of 1.27 kg CO 2 eq per kg waste mask. • The system is potential in carbon reduction by comparative analysis. [ABSTRACT FROM AUTHOR]

A novel co-valorization process integrating plasma gasification and Fischer-Tropsch synthesis is designed for converting medical waste (MW) and biomass waste (BMW) into mixed e-fuels where the additional required hydrogen is supplied by solar-based electrolysis. Optimization and comprehensive assessments have been conducted for three scenarios with different ratios of BMW. Operations optimization based on genetic algorithm (GA) has led to a remarkable enhancement in the quality of syngas, with a more than 10% increase in H 2 mole fraction. Techno-economic analysis reveals the net present values (NPVs) of three scenarios are −2.56 MM$, 3.38 MM$ and 40.1 MM$ with internal rates of return of 3.4 %, 9.5 % and 14.0 %. By improving the subside of MW treatment, the economic viabilities of all scenarios have been enhanced significantly with higher positive NPVs. Environmental assessment shows ∼2.5 kg eqCO 2 /kg waste and ∼0.1 g eqCO 2 per MJ fuel have been generated across the system boundary and the operation of the designed process has been identified to be the largest source of emissions. These findings underscore the potential of our integrated approach to efficiently convert MW and BMW into valuable e-fuels while maintaining a focus on environmental sustainability. [Display omitted] • Co-valorization process was designed for converting wastes into mixed e-fuel. • Plasma gasification and Fischer-Tropsch synthesis were integrated. • Scenarios with different blending ration were analyzed. • The H 2 fraction in syngas has been improved by over 10% via optimization. • Economic and environmental assessments were performed. [ABSTRACT FROM AUTHOR]

An innovative valorization process for biomass waste treatment has been developed in this study. The basic simulation process developed considering experimental studies, and it has been optimized by application of a pattern search algorithm to obtain the optimum output of energy efficiency, economic performance, and process safety index (PSI). Mainly, three different scenarios have been analyzed based on the sustainability index (SI). These SI has been determined through energy, economy, safety, and electric power potential. According to the results of the SI, the optimized processes are more sustainable (SI = 0.517 to 0.563) in all scenarios as compared to the basic process (SI = 0.503). Similarly, the economic analysis in terms of internal rate of return (%) of the basic process varies from 37 to 2% when the process efficiency dropped from 100 to 80%, and it varies from 44 to 6% for the optimized process without any subsidy. The optimized process has a potential of 2788–4044 kW of electric power generation from thermal energy of the process while it is only 2302 kW for basic scenario. Therefore, the optimized process has better energy, economic, electric power, and safety performances, thus it is more sustainable as compared with the basic process. [Display omitted] • Integration of pyrolysis and gasification for biomass valorization. • Pattern search algorithm application for valorization process optimization. • A novel sustainability index has been proposed. • Electricity potential from optimized process thermal energy is 2788–4044 kW. • Energy efficiency of the processes varies from 46% to 66%. [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]

In this work, a co-gasification process of sewage sludge and the poultry litter for hydrogen production was developed which involves gasification, water gas shift reaction, acid gas absorption and combustion for power generation. Based on the situations in Hong Kong, the complete processing system was simulated and optimized by developing a composite sustainability objective. The single objective was obtained by the fuzzy method after collecting the decision-making preferences on product yield, economic cost, safety index, environmental impacts, and exergy efficiency. Optimization results showed the exergy efficiency was 30.75%, and the gas turbine operation was most hazardous. The total life cycle greenhouse gas emission was estimated to be 832.58 tons/day with a hydrogen yield of about 47.92 tons/day according to the systematic analysis. Moreover, a subsidy of 115.65 $/ton waste can achieve a relatively attractive profit for the waste-to-H 2 process, which was validated by an IRR value of 10% and a positive NPV value in 20 years. The economic sensitivity analysis was finished by testing the influence of some key factors. It was indicated that the subsidy fees, H 2 market price, and total equipment cost were relatively more important in the financial profit for this process. [Display omitted] • A systematic waste co-valorisation process for H 2 production is proposed based on the gasification conversion. • Process optimization is finished by stochastic optimization with a constructed sustainability objetive. • Total life cycle GHG emission was 832.58 tons/day with a H 2 yield of about 47.92 tons/day. • The subsidy fees, H 2 market price, and total equipment cost is quite influential to the project economics. • A subsidy of 115.65 $/ton waste should be supported for good economic profit. [ABSTRACT FROM AUTHOR]