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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]

The upcycling of biomass waste gasification process for ethanol and acetaldehyde production has been augmented by integrating secondary and tertiary processes. A simulation model using Aspen Plus, developed from experimental investigations to assess the viability of this proposed system. Furthermore, this model has been utilized to create an Artificial Neural Network (ANN) prediction model. Process sustainability analysis demonstrated an energy efficiency of 64 % while economic viability up to 80 % process efficiency with an Internal Rate of Return (IRR) of 6 % and a payback period of 2107 days. An optimization strategy and an artificial neural network (ANN)-based predictive model have been developed. Optimization results revealed that a gasifier temperature of around 600 °C, a gasifying agent ratio of 2.0, and an acetaldehyde reactor temperature of 300 °C yield better process outcomes and revenue. The ANN model exhibited robust performance with coefficient of determination (R2) values ranging from 0.92 to 0.98 for acetaldehyde, hydrogen, and total revenue. Mean absolute error (MAE) and mean absolute percentage error (MAPE) fell within the range of 0.03–0.1 and 0.1–1.12 %, respectively. Consequently, given the favorable sustainability and predictive model performance, this study can be used for similar endeavors. [Display omitted] • Production of acetaldehyde from ethanol produced from biomass syngas. • Process IRR of 6 % and payback period of 2107 days at 80 % process efficiency. • ANN based prediction modelling with R2 of 0.92–0.98 • Energy efficiency of the process converting ethanol to acetaldehyde is 64 %. • Optimization of waste valorization process parameters. [ABSTRACT FROM AUTHOR]

[Display omitted] • Integrated process for blue and green hydrogen production through co-gasification. • Co-gasification process IRR of 8% at a process efficiency level of 70%. • Exergoeconomics costs of steam turbine and gasifier 6,561.3 $/h and 6,541.9 $/h. • Green hydrogen production through alkaline electrolysis cell (AEC) • Surplus electricity has a potential AEC green hydrogen production of 213.5 kg/day. A novel co-gasification process for biomass and plastic waste has been proposed to produce the blue and green hydrogen. For process feasibility, an Aspen Plus simulation model has been developed, and a sustainability analysis is being conducted, focusing on economic viability, exergy, advanced exergy considerations, and exergoeconomics evaluations. The current process has demonstrated economic sustainability, as evidenced by an internal rate of return (IRR) of 8 % at a process efficiency level of 70 %. The process with a waste capacity of 20 tons per hour has the potential to produce approximately 1079 kW-hours of electric power. The surplus electricity, exceeding the process requirements is utilized for green hydrogen production through an alkaline electrolysis cell (AEC). This surplus electricity has the potential to produce around 213.5 kg/day of hydrogen. The exergy analysis of this model highlights that the gasifier component exhibits the lowest exergy efficiency, resulting in the highest exergy loss, around 40 %. Furthermore, advanced exergy analysis identifies both the steam turbine and gasifier as primary sources of exergy destruction, with associated exergoeconomics costs of around $6,561.3 and $6,541.9 per hour, respectively. Consequently, improving the gasifier and steam turbine performance can enhance the overall sustainability of the process. [ABSTRACT FROM AUTHOR]

Gasification is one of the recommended processes for poultry litter valorization, and its success is largely dependent on process input parameters for syngas production. The quality of syngas, characterized by a higher heating value (HHV) and lower heating value (LHV), is significantly influenced by hydrogen. In this research, integration of Aspen Plus simulation and Convolutional Neural Network (CNN) have been done to estimate the hydrogen content in syngas. The gasification process has been optimized using the response surface method, and the results have been supported by the particle swarm optimization (PSO) technique. The statistical analysis of the response surface model revealed that the optimal process parameters are around 408 °C, 2.0 (biomass to air ratio) BMR, and 2.0 bars. Interestingly, PSO led to nearly identical optimum values (400 °C, 2.0 BMR, and 2.0 bars), resulting in high-quality syngas. Furthermore, CNN exhibited a good predicting performance with a coefficient of determination (R2) exceeding 0.96, coupled with mean square error (MSE) and mean absolute error (MAE) of 0.01 and 0.05, respectively. This solidifies the integration of Aspen Plus simulations and CNN as an accurate surrogate model for predicting hydrogen levels during poultry litter gasification, enabling effective process optimization. Therefore, the proposed model serves as a reliable tool for predicting and optimizing the syngas produced during the gasification process of poultry litter, with potential applications in enhancing energy production and waste management practices. • Hydrogen prediction in poultry litter gasification process through CNN. • Optimum parameters around 408 °C, 2.0 biomass to air ratio (BMR), and 2.0 bars. • BMR has a most significant effect on the amount of H 2 , HHV, and LHV of syngas. • CNN predicting performance with coefficient of determination (R2) exceeding 0.96. [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]

• Torrefaction, gasification, and SOFC based tri-generation process. • Economic, exergy, advanced exergy, and exergoeconomics analysis. • SOFC and steam-based power potential of 1331 kW per ton of biomass. • Gasifier and HeatXC have the lowest exergy efficiencies of 82 % and 79 %. • IRR of 20–13 % at 100–80 % of the process operational efficiency. An integrated novel process based on torrefaction, gasification, and solid oxide fuel cell (SOFC) has been developed for biomass waste valorization. Process sustainability analysis has been done through economic, exergy, advanced exergy, and exergoeconomics indicators. According to economic analysis, the internal rate of return (IRR) of this process dropped to 7% at 100–90% of the process efficiency and this process is not economically feasible below 90% operational efficiency. While the subsidized process IRR dropped from 20 to 13% at 100–80% of the process efficiency. Subsidized process is also not economically feasible below 80% process efficiency. The overall process electric power generation potential from SOFC and steam turbine generator is around 1331 kW. Gasifier and HeatXC have the highest exergy destructions (1110.7 and 1220.9 kW) with exergy efficiencies of around 82% and 79%, respectively. Gasifier, HeatXC, and SOFC have significant potential of exergy improvement due to higher avoidable exergy destruction. Therefore, process exergy and economic performance can be improved by targeting these components. [ABSTRACT FROM AUTHOR]

A tri-generation-based sustainable poultry litter (PL) valorization process has been developed in this study. To make the proposed process economically viable and energy efficient, the primary gasification process of PL is further enhanced to convert syngas into dimethyl ether (DME). According to our energy analysis, the current tri-generation process exhibits 57% energy efficiency, which is 12% higher than that of PL gasification (45%). A particle swarm optimization (PSO)-based algorithm was applied to optimize the gasification and DME production process. According to PSO results, the optimum operating conditions are 667 °C, 2 bar, and 1.78 air ratio in the gasification process, while using reaction temperatures of 400 and 100 °C in the DME reactors leads to 242.6 kg/ton DME with 4414.6 kJ/s net heat. On the contrary, only 190.8 kg/ton DME with 4642.2 kJ/s net heat can be generated in the base process. The optimized process is economically viable to achieve an 80% plant operational efficiency with an internal rate of return (IRR) of 16.3% when compared with the base process, which becomes infeasible when the plant efficiency is <90%. A sustainability index (SI) was developed, and the results show that the optimized process was more sustainable (0.290) than the base process (0.279). Therefore, our optimized DME process is more economically viable and eco-friendly. • An innovative process for poultry litter valorization is proposed. • The energy efficiency of the poultry litter tri-generation process is 57%. • Biomass gasification process is optimized by particles swarm optimization (PSO). • Life cycle assessment of dimethyl ether production from poultry litter. [ABSTRACT FROM AUTHOR]

Poultry litter can be converted into thermal or chemical energy which can assist in meeting the energy needs. The current study focuses on a critical review of the cause-and-effect analysis of hydrothermal gasification (HTG) process yield, safety, and economic analysis. HTG batch and continuous technologies have been investigated, each with its own set of constraints. For example, resident time in a continuous reactor ranges from a few seconds to minutes, whereas it ranges from several min to hours in a batch reactor. Process parameters temperature, pressure, resident time, and solid content also contribute to the syngas quality, but temperature has the most significant effect among all. Syngas quality can be improved by managing process parameters such as 500–550 °C, 25–28 MPa, 120–150 min resident time, and 10–20% of the solid biomass content. Catalyst application in HTG promotes more hydrogen production. HTG is environmentally sustainable as compared to direct land disposal of biomass. H 2 production costs range from $ 1.94 to 7.0 per kg and an investment payback period of 3.3–5.16 years. Process safety approaches that are appropriate to conduct HTG safety analysis have been investigated, and some control measures have been proposed which can be applied to make the HTG process safer. Finally, HTG future perspective and recommendations have been given based on current work. • Strengths and weaknesses of hydrothermal gasification process are summarized. • HTG optimum operating conditions are 500–550 °C, 25–28 MPa, 120–150 min, and 10–20% solid content. • Future perspective and recommendations are proposed for hydrothermal gasification process. • The safety and control measures of hydrothermal gasification process are analyzed. • Economic and environmental performances of hydrothermal gasification process are reviewed. [ABSTRACT FROM AUTHOR]

A novel configuration of a tri-generation process for poultry litter valorization, including gasification, solid oxide fuel cell (SOFC), and combined heat and power system was examined in this research. Multi-level factorial, design of experiment methodology has been adopted to extract the simulation data from Aspen Plus simulation model by changing one parameter at a time. Extreme gradient boosting has been applied on the factorial design data to predict and optimize the parametric yield of this model. Results of gasification process sensitivity analysis show that pressure has no significant effect on output yield, but it has a negative effect on SOFC voltage. While gasification process temperature operating condition around 600 °C and 0.25–0.33 biomass to air ratio (BMR) can generate optimum hydrogen yield in syngas. Coefficient of determinant (R2) for Extreme Gradient Booster (XGB) model is greater than 0.97 for all dependent variables. According to XGB results, BMR is the most contributing factor which affects the output of this study. Exergy efficiency of this tri-generation process is 34.6% more than the gasification process. Therefore, based on the findings of this model, it is concluded that this tri-generation process could be the better possible solution for poultry litter valorization. • 34.6% more exergy efficiency by tri-generation of poultry litter valorization. • Gasification process prediction by Extreme Gradient Boosting prediction model. • Optimum yield of H 2 at 600 °C and 0.25–0.33 biomass to air ratio. • Multilevel factorial design of experiment for gasification process optimization. • Solid Oxide Fuel Cell current and voltage calculations and prediction model. [ABSTRACT FROM AUTHOR]

The biomass poultry litter and sewage sludge co-gasification is a good thermochemical method to produce hydrogen energy meanwhile to mitigate the consumption of fossil fuels. However, the operation optimization in the complex process system is important while computationally difficult because of high nonlinearity and many first-principle constraints. To address the optimization of this biohydrogen production process, a methodology framework for achieving the optimal operations is thus presented. The complete process system is first simulated and decomposed into upstream gasification process and downstream hydrogen purification for reducing computational complexity through the thermodynamic equilibrium-based simulation. Totally 1400 data points by pairing total 15 operating conditions with a composite sustainability index objective are generated through the random sampling and data classification strategies, which are then used for the construction of the artificial neural network (ANN)-based prediction models. ANN models of two subprocesses demonstrate the satisfactory prediction accuracy with R2 value of 0.98 and 0.99, respectively, which are then integrated with mixed-integer linear programming (MILP) for the optimization of upstream process and downstream process step-by-step. The MILP problems based on the ANN models are solved with lower optimization time of 45∼100 s compared to the heuristic algorithm optimization (3–6 h) based on the thermodynamic equilibrium-based simulation. The optimal sustainability index values of two processes are 0.80 and 0.91 which are both improved compared to the existing optimization results (0.79 and 0.84). This study emphasizes the optimization potential of the integration approach of machine learning-based modelling and mathematical programming for developing the optimal waste-to-energy processes. [Display omitted] • A feasible data screening process is conducted based on ANN classification models. • The ANN regression model achieves good results with over 96 % prediction accuracy. • Only 98.54 and 46.36 s are needed to achieve the sub-processes optimization. • The process optimization based on data-driven models shows a much higher efficiency. [ABSTRACT FROM AUTHOR]