Design and economic optimization of shell and tube heat exchangers using Artificial Bee Colony (ABC) algorithm

In this study, a new shell and tube heat exchanger optimization design approach is developed. Artificial Bee Colony (ABC) has been applied to minimize the total cost of the equipment including capital investment and the sum of discounted annual energy expenditures related to pumping of shell and tube heat exchanger by varying various design variables such as tube length, tube outer diameter, pitch size, baffle spacing, etc. Finally, the results are compared to those obtained by literature approaches. The obtained results indicate that Artificial Bee Colony (ABC) algorithm can be successfully applied for optimal design of shell and tube heat exchangers.     

Thermodynamic analysis and optimization of a geothermal Kalina cycle system using Artificial Bee Colony algorithm

In this paper, thermodynamic analysis is carried out for a geothermal Kalina cycle employed in Husavic power plant. Afterwards, the optimum operating conditions in which the cycle is at its best performance are calculated. In order to reach the optimum thermal and exergy efficiencies of the cycle, Artificial Bee Colony (ABC) algorithm, a new powerful multi-objective and multi-modal optimization algorithm, is conducted. Regarding the mechanism of ABC algorithm, convergence speed and precision of solutions have been remarkably improved when compared to those of GA, PSO and DE algorithms. Such a relative improvement is indicated by a limit parameter and declining probability of premature convergence. In this research, exergy efficiency including chemical and physical exergies and thermal efficiency are chosen as the objective functions of ABC algorithm where optimum values of the efficiencies for the Kalina cycle are found to be 48.18 and 20.36%, respectively, while the empirical thermal efficiency of the cycle is about 14%. At the optimum thermal and exergy efficiencies, total exergy destruction rates are respectively 4.17 and 3.48 MW. Finally, effects of the separator inlet pressure, temperature, basic ammonia mass fraction and mass flow rate on the first and second law efficiencies are investigated.     

Dynamic optimization of a novel radial-flow,spherical-bed methanol synthesis reactor in the presence of catalyst deactivation using Differential Evolution (DE) algorithm

In this work, a novel radial-flow spherical-bed methanol synthesis reactor has been optimized using Differential Evolution (DE) algorithm. This reactor's configuration visualizes the concentration and temperature distribution inside a radial-flow packed bed with a novel design for improving reactor performance with lower pressure drop. The dynamic simulation of spherical multi-stage reactors has been studied in the presence of long-term catalyst deactivation. A theoretical investigation has been performed in order to evaluate the optimal operating conditions and enhancement of methanol production in radial-flow spherical-bed methanol synthesis reactor. The simulation results have been shown that there are optimum values of the reactor inlet temperatures, profiles of temperatures along the reactors and reactor radius ratio to maximize the overall methanol production. The optimization methods have enhanced additional yield throughout 4 years of catalyst lifetime, respectively.     

Differential evolution (DE) strategy for optimization of hydrogen production and utilization in a thermally coupled membrane reactor for decalin dehydrogenation and Fischer-Tropsch synthesis in GTL technology

In this study, the operating conditions of a thermally coupled membrane reactor (TCMR) in gas-to-liquid (GTL) technology are optimized via differential evolution (DE) method to maximize the hydrogen mole fraction in the endothermic side as well as the gasoline yield in the exothermic side. TCMR is designed as a double pipe reactor where highly exothermic Fischer-Tropsch synthesis (FTS) reactions in the exothermic side are coupled with decalin dehydrogenation reaction in the endothermic side. The minimum required hydrogen molar flow rate in the recycled stream is optimized to compensate a hydrogen lack at the end of the reactor in the exothermic side. The optimization results show 14.28% increase in the gasoline yield in optimized TCMR compared with conventional tubular reactor (CR). Moreover, 81.49% hydrogen is produced in the endothermic side and about 1% hydrogen is recycled to the exothermic side for utilization in FTS reactions in optimized TCMR.     

Differential evolution (DE) strategy for optimization of hydrogen production,cyclohexane dehydrogenation and methanol synthesis in a hydrogen-permselective membrane thermally coupled reactor

In this work a novel reactor configuration has been proposed for simultaneous methanol synthesis, cyclohexane dehydrogenation and hydrogen production. This reactor configuration is a membrane thermally coupled reactor which is composed of three sides for methanol synthesis, cyclohexane dehydrogenation and hydrogen production. Methanol synthesis takes place in the exothermic side that supplies the necessary heat for the endothermic dehydrogenation of cyclohexane reaction. Selective permeation of hydrogen through the Pd/Ag membrane is achieved by co-current flow of sweep gas through the permeation side. A steady-state heterogeneous model of the two fixed beds predicts the performance of this configuration. A theoretical investigation has been performed in order to evaluate the optimal operating conditions and enhancement of methanol, benzene and hydrogen production in a membrane thermally coupled reactor. The co-current mode is investigated and the optimization results are compared with corresponding predictions for a conventional (industrial) methanol fixed bed reactor operated at the same feed conditions. The differential evolution (DE), an exceptionally simple evolution strategy, is applied to optimize this reactor considering the mole fractions of methanol, benzene and hydrogen in permeation side as the main objectives. The simulation results have been shown that there are optimum values of initial molar flow rate of exothermic and endothermic stream, inlet temperature of exothermic, endothermic and permeation sides, and inlet pressure of exothermic side to maximize the objective function. The simulation results show that the methanol mole fraction in output of reactor is increased by 16.3% and hydrogen recovery in permeation side is 2.71 yields. The results suggest that optimal coupling of these reactions could be feasible and beneficial. Experimental proof-of-concept is needed to establish the validity and safe operation of the novel reactor.     

Structural optimization of biohydrogen production: Impact of pretreatments on volatile fatty acids and biogas parameters

The present study aims to describe an innovative approach that enables the system to achieve high yielding for biohydrogen (bio-H₂) production using xylose as a by-product of lignocellulosic biomass processing. A hybrid optimization technique, structural modelling, desirability analysis, and genetic algorithm could determine the optimum input factors to maximize useful biogas parameters, especially bio-H₂ and CH₄. As found, the input factors (pretreatment, digestion time and biogas relative pressure) and volatile fatty acids (acetic acid, propionic acid and butyric acid) had significantly impacted the biogas parameters and desirability score. The pretreatment factor had the most directly effect on bio-H₂ and CH₄ production among the factors, and the digestion time had the most indirectly effect. The optimization method showed that the best pretreatment was acidic pretreatment, digestion time > 20 h, biogas relative pressure in a range of 300–800 mbar, acetic acid in a range of 90–200 mg/L, propionic acid in a range of 20–150 mg/L, and butyric acid in a range of 250–420 mg/L. These values caused to produce H₂ > 10.2 mmol/L, CH₄ > 3.9 mmol/L, N₂ < 15.3 mmol/L, CO₂ < 19.5 mmol/L, total biogas > 0.31 L, produced biogas > 0.10 L, and accumulated biogas > 0.41 L.     

Differential evolution (DE) strategy for optimization of methane steam reforming and hydrogenation of nitrobenzene in a hydrogen perm‐selective membrane thermally coupled reactor

In this work, a thermally coupled membrane reactor is proposed for methane steam reforming and hydrogenation of nitrobenzene. The steam reforming process is carried out in the assisted membrane surface of the endothermic side, while the hydrogenation reaction of nitrobenzene to aniline is carried out on the other membrane surface of the exothermic side. The differential evolution (DE) strategy is applied to optimize this reactor considering nitrobenzene and methane conversion as the main objectives. The co‐current mode is investigated in this study, and the achieved optimization results are compared with those of conventional steam reformer reactor operated under the same feed conditions. The optimum values of feed temperature of exothermic side, feed molar flow rate of nitrobenzene, the steam‐to‐nitrobenzene molar ratio and the hydrogen‐to‐nitrobenzene molar ratio are determined during the optimization process. The simulation results show that the methane conversion and consequently hydrogen recovery yield are increased by 39.3% and 1.57, respectively, which contribute to aniline production with 27.3% saving in hydrogen supply from external and a reduction in environmental problems due to 100% nitrobenzene conversion. The optimization results justify the feasibility of coupling these reactions. Experimental proof‐of‐concept is needed to establish the validity and safe operation of the novel reactor. Copyright © 2012 John Wiley & Sons, Ltd.     

Elucidation of cobalt disturbance on Ni/Al2O3 in dissociating hydrogen towards improved CO2 methanation and optimization by response surface methodology (RSM)

In this study, nickel (Ni) and cobalt nickel (Co/Ni) supported on alumina were successfully synthesized by a facile electrolysis procedure and were tested for CO₂ methanation. By applying the Ni/Al₂O₃ catalyst, CO₂ conversion reached up to of 10 μmol/g.s, which is 1.4 times higher than Co/Ni/Al₂O₃, followed by the parent Al₂O₃. The addition of Co into Ni/Al₂O₃ has formed spinel phase in Co/Ni/Al₂O₃, as well as caused a slight increase in the basicity, which directed to the higher formation of formate species as observed by in-situ CO₂ + H₂ FTIR study. Both catalyst followed the dissociative mechanism during the CO₂ methanation. However, bigger metal particles in Co/Ni/Al₂O₃ caused slower hydrogen dissociation compared to Ni/Al₂O₃, leading to lower yield of CH₄. The optimization study via the response surface methodology (RSM) showed that the yield of CH₄ was significantly affected by reaction temperature, followed by treatment time, the ratio of H₂:CO₂ and lastly the gas hour space velocity (GHSV).     

Modeling and parametric optimization of air catalytic co-gasification of wood-oil palm fronds blend for clean syngas (H2+CO) production

Syngas production from biomass gasification is a potentially sustainable and alternative means of conventional fuels. The current challenges for biomass gasification process are biomass storage and tar contamination in syngas. Co-gasification of two biomass and use of mineral catalysts as tar reformer in downdraft gasifier is addressed the issues. The optimized and parametric study of key parameters such as temperature, biomass blending ratio, and catalyst loading were made using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) on tar reduction and syngas. The maximum H₂ was produced when Portland cement used as catalyst at optimum conditions, temperature of 900 °C, catalyst-loading of 30%, and biomass blending-ratio of W52:OPF48. Higher CO was yielded from dolomite catalyst and lowest tar content obtained from limestone catalyst. Both RSM and ANN are satisfactory to validate and predict the response for each type of catalytic co-gasification of two biomass for clean syngas production.     

Catalytic conversion of CO2 to biofuel (methanol) and downstream separation in membrane-integrated photoreactor system under suitable conditions

A heterogeneous photocatalyst has been developed using sono-chemical assisted sol-gel method by maintaining aweight ratio of 1:2:3 for hydrogen exfoliation graphene, titanium oxide andcopper sulphateand exhaustively characterized. Rigorous experimentations have been done using newly developed heterogeneous photocatalyst for efficient capturing and maximum conversion of carbon di oxide to methanol by mutual effects of governing conditions, like as catalyst dose, pH, CO₂ flow rate and temperature. Optimization study has been carried out employing a statistical approach of response surface methodology which reveals the maximum methanol productivity and yield. Approximately, 134 g/Lh of productivity and 40 mg/g_catof yield were found after 3 h of illumination under UV in an annular type Pyrex reactor at an optimum catalyst dosage of 10 g/L, CO₂ flow rate of 3 L/m, pH of 3, and process temperature of 50 °C. By the judicial integration of flat-sheet cross flow microfiltration membrane module for catalyst separation and recycle, a steady state permeate flux 145 L/m²h was achieved at an applied pressure of 3 bar and cross-flow feed rate of 700 L/h.     

Design and optimization of Isochoric Differential Apparatus (IDA) to reduce uncertainty in H2 sorption process measurements

The quantification of hydrogen absorption and desorption in materials is a crucial step for the assessment of proper storage solutions and their applications. Unfortunately, volumetric instruments are in many cases affected by low accuracy due to several factors such as temperature uncertainty and misleading on calibration proceeding.In this work, we report the superior performance of a new kind of instrumental layout to characterize kinetics and thermodynamics properties of hydrogen storage materials. Hereby presented system is based on differential Sievert measurements, defined as Isochoric Differential Apparatus (IDA). IDA includes two coupled identical Sievert apparatus where pressure values are sampled in differential mode to compensate all temperature transient phenomena and nonlinear effects occurring during the gas expansion step that occurs during the measurements. A physical model to evaluate the sorbed gas at non-isothermal condition has been developed and reported. Detailed error analysis of the kinetic and thermodynamic models has been carried out considering a real gas. Palladium and Magnesium has been utilized as benchmark materials, to test the differential apparatus at ambient and high-temperature values > 300 °C). For both materials, kinetic and thermodynamic properties have been acquired by the differential layout in well agreement with reference data and with a higher accuracy than classic Sievert instrument, involving in identical size of expansion volume. This work demonstrates as the differential layout allows to reduce uncertainty in hydrogen sorption measurement exploiting the full accuracy of equipped transducers. At this level of performance, the impact of calibration procedures and the approach for the estimation of compressibility factor become extremely important to further reduce uncertainty on sorption measurements.     

Performance evaluation of artificial neural network coupled with generic algorithm and response surface methodology in modeling and optimization of biodiesel production process parameters from shea tree (Vitellaria paradoxa) nut butter

This work investigated the potential of shea butter oil (SBO) as feedstock for synthesis of biodiesel. Due to high free fatty acid (FFA) of SBO used, response surface methodology (RSM) was employed to model and optimize the pretreatment step while its conversion to biodiesel was modeled and optimized using RSM and artificial neural network (ANN). The acid value of the SBO was reduced to 1.19 mg KOH/g with oil/methanol molar ratio of 3.3, H₂SO₄ of 0.15 v/v, time of 60 min and temperature of 45 °C. Optimum values predicted for the transesterification reaction by RSM were temperature of 90 °C, KOH of 0.6 w/v, oil/methanol molar ratio of 3.5, and time of 30 min with actual shea butter oil biodiesel (SBOB) yield of 99.65% (w/w). ANN combined with generic algorithm gave the optimal condition as temperature of 82 °C, KOH of 0.40 w/v, oil/methanol molar ratio of 2.62 and time of 30 min with actual SBOB yield of 99.94% (w/w). Coefficient of determination (R²) and absolute average deviation (AAD) of the models were 0.9923, 0.83% (RSM) and 0.9991, 0.15% (ANN), which demonstrated that ANN model was more efficient than RSM model. Properties of SBOB produced were within biodiesel standard specifications.     

Performance optimization of microbial electrolysis cell (MEC) for palm oil mill effluent (POME) wastewater treatment and sustainable Bio-H2 production using response surface methodology (RSM)

Microbial electrolysis cells (MECs) are a new bio-electrochemical method for converting organic matter to hydrogen gas (H₂). Palm oil mill effluent (POME) is hazardous wastewater that is mostly formed during the crude oil extraction process in the palm oil industry. In the present study, POME was used in the MEC system for hydrogen generation as a feasible treatment technology. To enhance biohydrogen generation from POME in the MEC, an empirical model was generated using response surface methodology (RSM). A central composite design (CCD) was utilized to perform twenty experimental runs of MEC given three important variables, namely incubation temperature, initial pH, and influent dilution rate. Experimental results from CCD showed that an average value of 1.16 m³ H₂/m³ d for maximum hydrogen production rate (HPR) was produced. A second-order polynomial model was adjusted to the experimental results from CCD. The regression model showed that the quadratic term of all variables tested had a highly significant effect (P < 0.01) on maximum HPR as a defined response. The analysis of the empirical model revealed that the optimal conditions for maximum HPR were incubation temperature, initial pH, and influent dilution rate of 30.23 ^°C, 6.63, and 50.71%, respectively. Generated regression model predicted a maximum HPR of 1.1659 m³ H₂/m³ d could be generated under optimum conditions. Confirmation experimentation was conducted in the optimal conditions determined. Experimental results of the validation test showed that a maximum HPR of 1.1747 m³ H₂/m³ d was produced.     

Effect of different gasifying agents (steam,H2O2, oxygen,CO2, and air) on gasification parameters

Two sensitivity analyses were performed in an Aspen simulation of fluidized bed gasification for five different gasifying agents such as steam, hydrogen peroxide (H₂O₂), pure oxygen (O₂), carbon dioxide (CO₂), and air. In the first sensitivity analysis, the modified equivalence ratio (MER) was varied (0.22-0.36). For the varied modified equivalence ratio (MER), %hydrogen, H₂/CO molar ratio, and hydrogen yield were the highest in steam-gasification, but %carbon monoxide, %methane, CO yield, and the lower heating values (LHV) were the highest in CO₂-gasification. In the second sensitivity analysis, the freeboard temperature was varied (500-900 °C). With increasing freeboard temperature, %hydrogen and %carbon monoxide increased while %carbon dioxide and %methane decreased for all the gasifying agents. Also, with increasing freeboard temperature, the LHV decreased and the hydrogen yield, CO yield, and the gas production rate increased for all the gasifying agents, but the H₂/CO molar ratio increased only in oxygen, air, and CO₂-gasification.     

Fabrication and characterization of poly(tannic acid) coated magnetic clay decorated with cobalt nanoparticles for NaBH4 hydrolysis: RSM-CCD based modeling and optimization

Hydrogen generation from sodium borohydride (NaBH₄) hydrolysis in the presence of metal catalysts is a frequently used and encouraging method for hydrogen storage. Metal nanoparticle-supported catalysts are better recyclability and dispersion than unsupported metal catalysts. In this study, the synthesis and characterization of a polymer-supported catalyst for hydrogen generation using NaBH₄ have been investigated. For the synthesis of polymeric material, first of all, kaolin (KLN) clay has been magnetically rendered by using the co-precipitation method (Fe₃O₄@KLN) and then coated with poly tannic acid (PTA@Fe₃O₄@KLN). Then, the catalyst loaded with cobalt (Co) nanoparticles have been obtained with the NaBH₄ reduction method (Co@PTA@Fe₃O₄@KLN). The surface morphology and structural properties of the prepared catalysts have been determined using methods such as scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), X-ray powder diffraction (XRD), X-ray photoelectron spectroscopy (XPS), inductively coupled plasma mass spectrometry (ICP-MS) and vibrating sample magnetometer (VSM). The optimization of the most important variables (NaBH₄ amount, NaOH amount, catalyst amount, and metal loading rate) affecting the hydrolysis of NaBH₄ using the synthesized polymeric catalysts was carried out using response surface methodology (RSM). Depending on the evaluated parameters, the desired response was determined to be hydrogen production rate (HGR, mL/g min). HGR was 1540.4 mL/g_cat. min. in the presence of the Co@PTA@Fe₃O₄@KLN at optimum points obtained via RSM (NaBH₄ amount 0.34 M, NaOH amount 7.9 wt%, catalyst amount 3.84 mg/mL, and Co loading rate 6.1%). The reusability performance of the catalyst used in hydrolysis of NaBH₄ was investigated under optimum conditions. It was concluded that the catalyst is quite stable.     

Modeling and multi-objective optimization of variable air gasification performance parameters using Syzygium cumini biomass by integrating ASPEN Plus with Response surface methodology (RSM)

The present study developed a robust method for the modeling and optimization of variable air gasification parameters using the ASPEN Plus simulator and Response surface methodology (RSM). A comprehensive thermochemical equilibrium based model of downdraft gasifier was developed by minimizing Gibbs free energy. Model validation was done by comparing the simulated result with the experimental result of four different feedstocks from the literature and, a good agreement was attained. The Complete modeling of the air gasification process was segregated into four phases viz. biomass drying, biomass decomposition, biomass gasification, and producer gas filtration. Drying operation and yield distribution during pyrolysis were computed by incorporating FORTRAN sub-routine statement. Sensitivity analysis was performed to obtain syngas composition using Syzygium cumini biomass fuel and different gasification performances like gas yield (GY), cold gas efficiency (CGE), and higher heating value (HHV) using gasification temperature (600–900)⁰C and equivalence ratio (ER) (0.2–0.6). Furthermore, RSM has been employed for the multi-objective optimizations of the variable gasification parameter. Central composite design (CCD) is adopted. Two independent parameters viz. temperature and equivalence ratio have opted as decision parameters for estimating the optimum performance parameters i.e., hydrogen concentrations, CGE, and HHV. Regression models created from the ANOVA results are found to be highly accurate in predicting output response variables. The optimal values of H₂, CGE, and HHV are found to be 0.1 (mole frac), 25.23%, and 3.96 MJ/kg respectively corresponding to optimized temperature at 887.879 °C and equivalence ratio 0.32 using response optimizer. The composite desirability observed was 0.59.     

Multiobjective optimization and analytical comparison of single‐ and 2‐stage (series/parallel) thermoelectric heat pumps

The thermodynamic modeling of various thermoelectric devices does not consider the influence of nonlinear Thomson effect. It leads to incomplete solutions of the heat transfer equations and serious analytical errors. On the other hand, appropriate balance among various performance parameters of thermoelectric devices is also required to improve its operating characteristics. In this context, the thermodynamic modeling on the basis of first/second laws for multielement single‐ and 2‐stage (series/parallel) thermoelectric heat pumps, including the influence of Thomson effect in combination with Fourier heat conduction and Joule effects, is done. The optimization of the heat pumps has been carried out to obtain the optimal values of 4 input parameters by using the second version of nondominated sorting genetic algorithm in matrix laboratory. The optimal values from Pareto frontier of dual/triple objectives are obtained through 3 decision makings viz. Shannon's entropy, Fuzzy Bellman‐Zadeh, and TOPSIS. It is observed that triple‐objective optimization gives much lower difference between ideal and obtained solution, termed as deviation index, as compared with the single/dual one. Additionally, sensitivity analysis has been carried out to study the influence of Thomson effect on heating capacity and coefficient of performance of the pumps. To validate the evolutionary algorithm, the optimal values are compared with analytical ones from previous literature.     

Density functional and microkinetic study of CO2 hydrogenation to methanol on subnanometer Pd cluster doped by transition metal (M= Cu,Ni, Pt,Rh)

We study the CO₂ hydrogenation to methanol on subnanometer Pd₇ and transition metal doped Pd₆M (M = Cu, Ni, Pt, and Rh) clusters using a combination of density functional theory and microkinetic calculations. We find that, in general, the inclusion of transition metal dopants could decrease the activation energy of several important elementary reactions. This condition results in a significant improvement in the activity of the catalyst, especially for the Pd₆Ni cluster. We find that the Pd₆M clusters are more selective toward the formate pathway than the RWGS + CO hydrogenation pathway. We also compare the turnover frequency profiles of the clusters with that of the Cu(111) surface, representing the standard industrial catalyst. We find that the Pd₆Ni cluster can successfully overcome the TOF of Cu(111) surface, even at the low-pressure condition.     

Performance analysis and optimization design of an axial‐flow vane separator for supercritical CO2 (sCO2)‐water mixtures from geothermal reservoirs

Supercritical CO₂ (sCO₂) has been proven to be a promising working fluid for geothermal heat mining, and the produced hot sCO₂ can be directly used for power generation. However, the sCO₂ produced from a brine‐based reservoir may contain a certain amount of water, preventing direct power‐cycle utilization. In this paper, an axial vane separator was designed to address the separation problem of sCO₂ and water produced from geothermal reservoirs. First, the influences of operational and structural parameters on the separation performance were analyzed through numerical simulations. Five factors were selected to develop separation performance regression models by the response‐surface method (RSM). Finally, geometrical parameter optimization was applied to these RSM models. The results show that the guide vane area and the exhaust inlet are the main locations impacting the system pressure drop. The separation performance can be affected by many factors, including the guide blade outlet angle, number of vanes, hub diameter, length of the vortex tube, droplet size, and inlet velocity. The water‐droplet size and the number of vanes are the most critical factors affecting the separation efficiency. The inlet velocity, the number of vanes, and the hub diameter have a larger influence on the pressure drop of the separator. The optimization results indicate that the separation efficiency can reach 100% under certain operating conditions with a pressure drop no greater than 100 kPa.