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In this article, we study the convergence behavior of G-nonexpansive mappings for three-step iterations in convex metric space along with the directed graph. We demonstrate the Δ-convergence as well as the strong convergence results for these mappings. For supporting our main results we presented a numerical example and convergence comparison of modified Thianwan iteration with SP iteration scheme and Noor iteration scheme.

Torrefied biomass is a vital green energy source with applications in circular economies, addressing agricultural residue and rising energy demands. In this study, ML models were used to predict durability (%) and mass loss (%). Firstly, data was collected and preprocessed, and its distribution and correlation were analyzed. Gaussian Process Regression (GPR) and Ensemble Learning Trees (ELT) were then trained and tested on 80 % and 20 % of the data, respectively. Both machine learning models underwent optimization through Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for feature selection and hyperparameter tuning. GPR-PSO demonstrates excellent accuracy in predicting durability (%), achieving a training R2 score of 0.9469 and an RMSE value of 0.0785. GPR-GA exhibits exceptional performance in predicting mass loss (%), achieving a training R2 value of 1 and an RMSE value of 9.7373e-05. The temperature and duration during torrefaction are crucial variables that are in line with the conclusions drawn from previous studies. GPR and ELT models effectively predict and optimize torrefied biomass quality, leading to enhanced energy density, mechanical properties, grindability, and storage stability. Additionally, they contribute to sustainable agriculture by reducing carbon emissions, improving cost-effectiveness, and aiding in the design and development of pelletizers. This optimization not only increases energy density and grindability but also enhances nutrient delivery efficiency, water retention, and reduces the carbon footprint. Consequently, these outcomes support biodiversity and promote sustainable agricultural, ecosystem, and environmental practices.

Abstract The swift advancement of sustainable energy technologies, coupled with the urgent need to address environmental challenges, has generated considerable interest in the multifaceted applications of biochar materials to promote energy, water, and environmental sustainability. This comprehensive review examines recent advancements in the production and applications of functionalized biochar materials, emphasizing their pivotal roles in energy conversion and storage, wastewater treatment, CO2 reduction, soil amelioration, and the promotion of carbon neutrality within a circular economy framework. The functionalization of biochar materials involves surface chemistry and porosity modifications, achieved through techniques like templating, chemical activation, metal impregnation, or heteroatom doping. These modifications substantially enhance the catalytic activity, energy storage capacity, and cycling stability of biochar materials, making them particularly effective in diverse energy applications such as water splitting, fuel cells, and supercapacitors. Additionally, functionalized biochar materials demonstrate remarkable efficacy as catalysts and adsorbents in wastewater treatment, proficiently removing pollutants like heavy metals, organic contaminants, and nutrients, thereby facilitating resource recovery from wastewater. The review also underscores the potential of functionalized biochar materials in CO2 capture and conversion, exploring innovative strategies to augment their CO2 adsorption capacity and state-of-the-art catalytic processes for transforming captured CO2 into valuable fuels and chemicals. In summary, this review offers valuable insights into the recent advancements in biochar research, underscoring its substantial commercial potential as a versatile material contributing to a cleaner and more sustainable future. Article Highlights The current status of biochar research is comprehensively reviewed. The potential of biochar in energy, water, and environmental fields is critically examined. Technology readiness levels (TRLs) of various biochar-based technologies are evaluated. Graphical Abstract

This study focused on the modeling and simulation of the Ethylene Oxide (EO) production process in Aspen plus software and energy optimization of the whole process using the Pinch analysis. This work focused on ethylene oxide production by the air-based system and modeled and simulated the whole process in Aspen plus® using the S.R.K. and PSRK thermodynamic models. By process design, modeling and simulation, the behavior of the actual process has been predicted. After process simulation, it has been concluded that control the oxygen concentration by controlling the airflow and controlling the reactor's temperatures causes an impact on our desired product. When the oxygen concentration increases, ethylene is oxidized completely and generated useless products such as carbon dioxide and water to enhance production. Energy optimization of ethylene oxide production is completed through Pinch analysis and saves 18.27million $ per year, which is consumed in heating or cooling streams.

The Claus process is one of the promising technologies for acid gas processing and sulfur recovery. Hydrogen sulfide primarily exists as a byproduct in the gas processing unit. It must be removed from natural gas. The Environmental Protection Agency (EPA) notices that increasing SO2 and CO2 in the air harms the environment. Sulfur generally has an elemental content of 0.1–6 wt % in crude oil, but the value could be higher than 14% for some crude oils and asphalts. It produces SO2 and CO2 gases, which damage the environment and atmosphere of the earth, called primary pollutants. When SO2 gas is reacted with water in the atmosphere, it causes sulphur and nitric acid, called a secondary pollutant. The world countries started desulphurization in 1962 to reduce the amount of sulfur in petroleum products. In this research, the Claus process was modeled in Aspen Plus software (AspenTech, Bedford, MA, USA) and industrial data validated it. The Peng–Robinson method is used for the simulation of hydrocarbon components. The influence of oxygen gas concentration, furnace temperature, the temperature of the first catalytic reactor, and temperature of the second catalytic reactor on the Claus process were studied. The first objective of the research is process modeling and simulation of a chemical process. The second objective is optimizing the process. The optimization tool in the Aspen Plus is used to obtain the best operating parameters. The optimization results show that sulfur recovery increased to 18%. Parametric analysis is studied regarding operating parameters and design parameters for increased production of sulfur. Due to pinch analysis on the Claus process, the operating cost of the heat exchangers is reduced to 40%. The third objective is the cost analysis of the process. Before optimization, it is shown that the production of sulfur recovery increased. In addition, the recovery of sulfur from hydrogen sulfide gas also increased. After optimizing the process, it is shown that the cost of heating and cooling utilities is reduced. In addition, the size of equipment is reduced. The optimization causes 2.5% of the profit on cost analysis.

This study focuses on developing four machine learning (ML) models (Gaussian process regression (GPR), support vector machine (SVM), decision tree (DT), and ensemble learning tree (ELT)) optimized and hyperparameters tuned via genetic algorithm (GA) and particle swarm optimization (PSO) to analyze and predict the adsorption capacity of four estrogenic hormones. These hormones are a serious cause of fish femininity and various forms of cancer in humans. Their adsorption via electrospun nanofibers offers a sustainable and relatively environmentally friendly solution compared to nanoparticle adsorbents, which require secondary treatment. The intricate task is to find the relationship between input parameters to obtain optimum conditions, which requires an efficient ML model. The GPR integrated GA hybrid model performed the most accurate and precise results with R2 = 0.999 and RMSE = 2.4052e−06, followed by ELT (0.9976 and 4.3458e−17), DT (0.9586 and 2.4673e−16), and SVM (0.7110 and 0.0639). The 2D and 3D partial dependence plots showed temperature, dosage, initial concentration, contact time, and pH as vital adsorption parameters. Additionally, Shapley's analysis further revealed time and dosage as the most sensitive parameters. Finally, a user‐friendly graphical user interface (GUI) was developed as a predictor utilizing the optimum hybrid model (GPR‐GA), and the results were experimentally validated with a maximum error of < 3.3% for all tests. Thus, the GUI can legitimately work for any desired material with given input conditions to efficiently monitor the removal concentration of all four estrogenic hormones simultaneously at wastewater treatment plants. [ABSTRACT FROM AUTHOR]

The Claus process is one of the promising technologies for acid gas processing and sulfur recovery. Hydrogen sulfide primarily exists as a byproduct in the gas processing unit. It must be removed from natural gas. The Environmental Protection Agency (EPA) notices that increasing SO2 and CO2 in the air harms the environment. Sulfur generally has an elemental content of 0.1–6 wt % in crude oil, but the value could be higher than 14% for some crude oils and asphalts. It produces SO2 and CO2 gases, which damage the environment and atmosphere of the earth, called primary pollutants. When SO2 gas is reacted with water in the atmosphere, it causes sulphur and nitric acid, called a secondary pollutant. The world countries started desulphurization in 1962 to reduce the amount of sulfur in petroleum products. In this research, the Claus process was modeled in Aspen Plus software (AspenTech, Bedford, MA, USA) and industrial data validated it. The Peng–Robinson method is used for the simulation of hydrocarbon components. The influence of oxygen gas concentration, furnace temperature, the temperature of the first catalytic reactor, and temperature of the second catalytic reactor on the Claus process were studied. The first objective of the research is process modeling and simulation of a chemical process. The second objective is optimizing the process. The optimization tool in the Aspen Plus is used to obtain the best operating parameters. The optimization results show that sulfur recovery increased to 18%. Parametric analysis is studied regarding operating parameters and design parameters for increased production of sulfur. Due to pinch analysis on the Claus process, the operating cost of the heat exchangers is reduced to 40%. The third objective is the cost analysis of the process. Before optimization, it is shown that the production of sulfur recovery increased. In addition, the recovery of sulfur from hydrogen sulfide gas also increased. After optimizing the process, it is shown that the cost of heating and cooling utilities is reduced. In addition, the size of equipment is reduced. The optimization causes 2.5% of the profit on cost analysis. [ABSTRACT FROM AUTHOR]