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A model based on simulated annealing approach is used with e-UNIQUAC model as well as two other algorithms to study the absorption of carbon dioxide by monoethanolamine solutions. In this work, we propose to compare the performance (through the knowledge of RMSD values) of two stochastic methods, namely the GA (genetic algorithm) and SA (simulated annealing) methods and a deterministic method that is the simplex method. These methods were applied to the absorption of carbon dioxide by an alkanolamine solution using a chemical equilibrium model and a thermodynamic equilibrium model. The latter is based on the use of the modified-UNIQUAC (UNIQUAC-electrolyte) model instead of e-NRTL model for the liquid phase and a fugacity model for the vapor phase. The chemical equilibrium in this work represents the absorption of CO 2 by monoethanolamine solution at different temperatures. Solving the coupled system of material and charge balances gives us the carbon dioxide pressure while taking into account the non-ideality of the system. The three-optimization methods show good agreement with the experimental data with a better performance of the simulated annealing method..

Drying kinetics, energy utilization (EU) and shrinkage level of avocado pear pulp during drying were investigated and modeled to determine the condition that enhances the quality of the dried product. Drying was carried out using a microwave assisted dryer with data lodger. The system was set at a constant power of 200 W, air velocity of 1.4 m/s, and temperatures of 50, 60 and 70 °C with pulp thickness being 5 mm. Fifteen thin-layer drying models, five non-linear shrinkage models and ANN methods were tested for describing the drying behaviour of avocado pulp using statistical parameters. The results revealed that drying took place in the falling rate period with the above temperatures reducing the moisture content of the pulp from 64.12 to 2.16 % wet basis within 15,360, 11,520 and 5,130 s, respectively. The drying rate and effective diffusivity increased with increase in temperature and ranged from 6.05 × 10 −3 to 1.70 × 10 −2  kg/kgs and 3.11 to 9.34 × 10 −9  m 2 /s, respectively. The activation energy of the pulp was 50.34 kJ/mol. Among the drying models tested, Page and Aghashilo models provided the best statistical parameters for describing the drying behaviour of the pulp, while ANN demonstrated great ability to predict MR and SR more accurately with high and low R 2 and RMSE. A non-linear shrinkage model developed also had the best fit qualities for describing the shrinkage behaviour of the pulp. The energy utilized (EU) , specific energy utilized (S EU ) , heat transfer coefficient (h tc ) and mass transfer coefficient (M tc ) of the pulp ranged from 7.36 to 3.19 kWh, 11.21 to 5.76 × 10 −2  Wh/kg, 0.1054 to 7.98 × 10 −7  W/mK and 2.06 to 4.28 × 10 −6  m/s respectively and were statistically (5 %) influenced by temperature. The EU model developed had the best description behaviour of the energy relationship with other factors, having high R 2 and low RMSE and SSE values.

Plasma gasification is a viable and efficient technique for handling solid waste, including hazardous waste, while also holding the potential for energy recovery. The objective of this study is to analyze the treatment of municipal solid waste (MSW) using plasma gasification by application of Aspen Plus software. An earlier proposed model was used to analyze the effect of employing different types of gasifying agents as plasma gas, on the composition of syngas produced. The lower heating value, cold gasification efficiency and carbon conversion efficiency were calculated and compared in each case. A sensitivity analysis study was also carried out to observe the effect of variation in plasma gas flow rate and feed flow rate on the composition of syngas generated. The capital, operational and maintenance costs of the process were determined using existing correlations. For a feed rate of 20,000 kg per hour of MSW, the highest yield of syngas (CO (33.48 %), and H 2 (34.30 %) with the highest LHV (7.9 MJ/N-m 3 )) were produced when air was employed as plasma gas. The cold gas efficiency and the carbon conversion efficiency were at their peak when air was used as the gasifying agent. The sensitivity analysis revealed that as the flow rate of plasma gas increases syngas production decreases while the increase in MSW flow rate results in an increase in syngas production. Additionally, the cost analysis revealed that for a plasma gasification plant that can handle 500 tons of MSW per day, the estimated capital and annual operational and maintenance costs are $125,496,721 and $9,833,892 respectively.

Experiments, as well as numerical simulations, were conducted to study discharge behavior of Microcrystalline Cellulose (MCC) in a flat-bottom silo. The three different types of openings, viz. concentric orifice, off-center orifice and two orifices were used. In the case of a concentric orifice, the mass flow rate is higher than the off-center orifice and two orifices. When the diameter of the orifice remains constant, an inverse relationship is observed between particle size and recorded flow rates, indicating that larger particles result in lower flow rates. The percentage decrease in mass flow rate (MFR) in off-center and double orifices has been compared with concentric orifices. We observed 8.5 % decrease in MFR for MCC 350 using a double orifice where as a 11 % decrease for MCC 700 (MCC 700 particle size is twice that of MCC 350) and 24 % decrease for MCC 1000 (MCC 1000 particle size is 2.8 times that of MCC 350). With an increase in particle size, the percentage decrease in MFR in double orifice increases, while in the case of off-center orifices, it decreases. Segregation is taking place due to percolation in binary mixtures through all discharge orifices. The extent of segregation in the case of the double orifice is more compared to concentric and off-center orifices. We observed the excess fine flow using double orifice for sample A and B up to 40 % discharge of mass and for sample C and D up to 50 % discharge of mass.

Compared to fossil fuels, biomass fuels have minimal sulfur content, lower ash production, and significantly reduced emissions. The global need to reduce dependence on imported energy sources and preserve dwindling fossil fuel reserves underscores the importance of utilizing alternative energy resources. Biomass, with its abundant availability, presents a promising source for syngas production, even though the gasification procedure requires substantial energy due to its endothermic nature. Challenges related to the efficiency of biomass gasification and compliance with environmental standards have hindered economic viability. Much attention has been focused on predictive modeling of biomass gasification procedures to address these issues, necessitating robust frameworks capable of predicting parameters under varying operating conditions. This article introduces two hybrid frameworks, which are combined versions of Adaptive Neuro-Fuzzy Inference System (ANFIS) with Artificial Rabbits Optimization (ARO) and Crystal Structure Algorithm (CSA), based on proximate biomass values to predict elemental compositions (N 2 and H 2 ). These intelligent hybrid frameworks, trained with 70 % of biomass data, were further validated and tested with the remaining 15 % portions of the database. The frameworks were assessed based on some known performance metrics, namely, root mean squared error (RMSE), mean absolute error (MAE), coefficient of determination ( R 2 ), RMSE-observations standard deviation ratio (RSR), and Nash-Sutcliffe Efficiency (NSE). Developed single and two hybrid frameworks compared and obtained outcomes revealed that both introduced optimizers efficiently promoted N 2 and H 2 estimation by ANFIS, especially CSA. R 2 values for ANCS were a maximum of 0.993 in both targets’ predictions. Also, minimum RMSE values of 1.007 and 1.470 related to N 2 and H 2 prediction emphasized the accuracy of ANCS, which is capable of being used in real-world applications.

Salt adsorption from water onto the activated carbon was studied by a set of batch adsorption tests. Isotherms (Langmuir, Freundlich, Halsey and Redlich-Peterson) were used to study the experimental data for the adsorption isotherm analyzed. For magnesium, R 2 is equal to 0.98 for all isotherms, and for calcium, the Langmuir value is 0.97, while for sodium, the Langmuir value is 0.98, which is more suitable than the other isotherms. The experimental data were examined using three kinetic models, including first-second-order and intra-diffusion ones with R 2 value of 0.96, 0.67 and 0.93 respectively. According to the kinetic models, the first -order isotherm model better fit adsorption on the surface of activated carbon, as compared to other models. Similarly, the results of the experiment were provided via the computational fluid dynamics evaluation. Moreover, the results obtained by CFD were compared with the experimental data, and their accuracy was proved. Subsequently, the effects of changing the design and operating parameters, including flow rate (6, 12, 30 L min −1 ) and bed height (5, 10, 20 cm), on the performance of this tower were studied. The results showed that by reducing the adsorbent, the adsorbed metals increased and a longer bed was required for adsorption, which was not cost-effective. The amount of adsorption decreased as the flow rate increased, indicating that there was little contact between the metals and the adsorbent.

Control strategies designed for step signals fail when applied for ramp tracking and ramp disturbance rejection. Hence, this work presents a novel dual-loop control technique for ramp tracking and ramp disturbance rejection in unstable systems. To begin with, first the unstable process is stabilized using a proportional-derivative (PD) compensator (in the internal loop). This PD compensator was created utilising the direct synthesis approach. Using the loop shaping approach, a proportional-integral-derivative controller (in the outer-loop) is then developed to integrate stabilised plant dynamics. Simulations are done using standard unstable CSTR (Continuous Stirred Tank Reactor) plant models by applying step/ramp reference signals and disturbances. The proposed control strategy shows a satisfactory servo and regulatory response than the existing designs while dealing with step and ramp types of signals. Lastly, a performance summary is also presented on different errors.

This study explores the design, dynamic behavior, and controllability of a hybrid membrane-distillation process for propane/propylene separation, offering a novel comparison with traditional single distillation columns. Our key innovation lies in integrating hollow fiber membrane technology with distillation to significantly improve process efficiency, reducing energy consumption and operational costs. Using rigorous economic and dynamic simulations, the hybrid process demonstrates superior performance, achieving a 12.6 % reduction in total annual cost (TAC) compared to the conventional distillation system. Advanced control methodologies, including Singular Value Decomposition (SVD) and condition number analysis, are applied to assess and optimize the controllability of the hybrid structure under disturbances such as fluctuations in feed composition and flow rates. The results highlight the hybrid process’s ability to maintain high product purity and faster dynamic response, offering a robust solution for large-scale propane/propylene separation in the petrochemical industry. This work provides crucial insights into improving both operational efficiency and control robustness in industrial separation processes.

To get better industrial equipment, one must understand the different phenomena within a machine. Non-equilibrium condensation is one of the natural phenomena happening in the process, thereby affecting flow behavior; the concept is vital in the understanding and optimizing machinery applied in industries. The present investigation focuses on the surface heating method, more precisely using the constant heat flux in the context of NEC inside a supersonic nozzle. The results indicate that the heat flux method can delay the droplet nucleation and growth inside the nozzle. However, this should be considered to a limit due to the raise of temperature at the nozzle wall. The rise in heat flux has reduced the Liquid Mass Fraction (LMF) within the nozzle. The LMF at the nozzle outlet and center line is determined to be 0.075, 0.072, and 0.068 for the adiabatic condition, 200 kW/m 2 flux, and 400 kW/m 2 flux, respectively. The heat flux also influences the flow pattern. With an increase in heat flux, the condensation shock wave is shifted downstream, decreasing its intensity.

The machine learning methods are hereby proposed to predict the amount of Carbon Monoxide (CO) and Carbon Dioxide (CO₂) emissions in a gasification process, which is one of the most important enabling technologies for carbon-containing materials, such as coal, biomass, and waste toward producing end products of worth, such as syngas, hydrogen, and synthetic fuels. In an attempt to support efforts for improving the emission prediction-a key criterion for enhancing efficiency and further, the environmental performance of gasification-two new advanced algorithms are being applied for the optimization of the model of a random forest: the Jellyfish Search Optimizer (JSO) and Sooty Tern Optimization Algorithm (STOA). The tuned RFJS (RF+JSO) was the best of these configurations, providing the least RMSE of 0.593 on test data and the highest R 2 on validation of 0.983, proving to be most effective for the prediction of emissions. This goes to attest that the model RFJS would be a strong tool in real-time-based carbon emissions reduction due to its effectiveness in dealing with major implications from environmental monitoring to regulation and further into sustainable energy production.