A novel 3-layer mixed cultural evolutionary optimization framework for optimal operation of syngas production in a Texaco coal-water slurry gasifier

Optimizing operational parameters for syngas production of Texaco coal-water slurry gasifier studied in this paper is a complicated nonlinear constrained problem concerning 3 BP(Error Back Propagation) neural networks. To solve this model, a new 3-layer cultural evolving algorithm framework which has a population space, a medium space and a belief space is firstly conceived. Standard differential evolution algorithm(DE), genetic algorithm(GA), and particle swarm optimization algorithm(PSO) are embedded in this framework to build 3-layer mixed cultural DE/GA/PSO(3LM-CDE, 3LM-CGA, and 3LM-CPSO) algorithms. The accuracy and efficiency of the proposed hybrid algorithms are firstly tested in 20 benchmark nonlinear constrained functions. Then, the operational optimization model for syngas production in a Texaco coal-water slurry gasifier of a real-world chemical plant is solved effectively. The simulation results are encouraging that the 3-layer cultural algorithm evolving framework suggests ways in which the performance of DE, GA, PSO and other population-based evolutionary algorithms(EAs) can be improved,and the optimal operational parameters based on 3LM-CDE algorithm of the syngas production in the Texaco coalwater slurry gasifier shows outstanding computing results than actual industry use and other algorithms.

Oxidative reforming of methane for hydrogen and synthesis gas production:Thermodynamic equilibrium analysis

A thermodynamic analysis of methane oxidative reforming was carried out by Gibbs energy minimization （at constant pressure and temperature） and entropy maximization （at constant pressure and enthalpy） methods,to determine the equilibrium compositions and equilibrium temperatures,respectively.Both cases were treated as optimization problems （non-linear programming formulation）.The GAMS 23.1 software and the CONOPT2 solver were used in the resolution of the proposed problems.The hydrogen and syngas production were favored at high temperatures and low pressures,and thus the oxygen to methane molar ratio （O 2 /CH 4） was the dominant factor to control the composition of the product formed.For O 2 /CH 4 molar ratios higher than 0.5,the oxidative reforming of methane presented autothermal behavior in the case of either utilizing O 2 or air as oxidant agent,but oxidation reaction with air possessed the advantage of avoiding peak temperatures in the system,due to change in the heat capacity of the system caused by the addition of nitrogen.The calculated results were compared with previously published experimental and simulated data with a good agreement between them.

Machine learning-driven optimization of plasma-catalytic dry reforming of methane

This study investigates the dry reformation of methane(DRM)over Ni/Al_(2)O_(3)catalysts in a dielectric barrier discharge(DBD)non-thermal plasma reactor.A novel hybrid machine learning(ML)model is developed to optimize the plasma-catalytic DRM reaction with limited experimental data.To address the non-linear and complex nature of the plasma-catalytic DRM process,the hybrid ML model integrates three well-established algorithms:regression trees,support vector regression,and artificial neural networks.A genetic algorithm(GA)is then used to optimize the hyperparameters of each algorithm within the hybrid ML model.The ML model achieved excellent agreement with the experimental data,demonstrating its efficacy in accurately predicting and optimizing the DRM process.The model was subsequently used to investigate the impact of various operating parameters on the plasma-catalytic DRM performance.We found that the optimal discharge power(20 W),CO_(2)/CH_(4)molar ratio(1.5),and Ni loading(7.8 wt%)resulted in the maximum energy yield at a total flow rate of∼51 mL/min.Furthermore,we investigated the relative significance of each operating parameter on the performance of the plasma-catalytic DRM process.The results show that the total flow rate had the greatest influence on the conversion,with a significance exceeding 35%for each output,while the Ni loading had the least impact on the overall reaction performance.This hybrid model demonstrates a remarkable ability to extract valuable insights from limited datasets,enabling the development and optimization of more efficient and selective plasma-catalytic chemical processes.

Atomically Dispersed Fe-Co Bimetallic Catalysts for the Promoted Electroreduction of Carbon Dioxide

The electroreduction reaction of CO_(2)(ECO_(2)RR)requires high-performance catalysts to convert CO_(2)into useful chemicals.Transition metal-based atomically dispersed catalysts are promising for the high selectivity and activity in ECO_(2)RR.This work presents a series of atomically dispersed Co,Fe bimetallic catalysts by carbonizing the Fe-introduced Co-zeolitic-imidazolate-framework(C-Fe-Co-ZIF)for the syngas generation from ECO_(2)RR.The synergistic effect of the bimetallic catalyst promotes CO production.Compared to the pure C-Co-ZiF,C-Fe-Co-ZIF facilitates CO production with a CO Faradaic efficiency(FE)boost of 10%,with optimal FE_(CO)of 51.9%,FE_(H_(2))of 42.4%at-0.55 V,and CO current density of 8.0 mA cm^(-2)at-0.7 V versus reversible hydrogen electrode(RHE).The H_(2)/CO ratio is tunable from 0.8 to 4.2 in a wide potential window of-0.35 to-0.8 V versus RHE.The total FE_(CO+H_(2))maintains as high as 93%over 10 h.The proper adding amount of Fe could increase the number of active sites and create mild distortions for the nanoscopic environments of Co and Fe,which is essential for the enhancement of the CO production in ECO_(2)RR.The positive impacts of Cu-Co and Ni-Co bimetallic catalysts demonstrate the versatility and potential application of the bimetallic strategy for ECO_(2)RR.

Experimental studies of biomass gasification with air

In this work, experimental studies of biomass gasification under different operating conditions were carried out in an updraft gasifier combined with a copper slag reformer. The influence of gasification temperature, equivalence ratio （ER） and copper slag catalyst addition on gas production and tar yield were investigated. The experimental results showed that the content of H2 and CO, gas yield and LHV increased, while the tar yield and the content of CO2, CH4 and C2Hx in the gas product decreased with the temperature. At 800℃, with the increase of ER, the LHV, the tar yield and the content of H2, CO, CH4 and C2H2 in gas products decreased, while the gas yield and the content of CO2 increased. Copper slag was introduced into the secondary reformer for tar decomposition. The Fe3O4 phase in the fresh copper slag was reduced to FeO （Fe^2＋） and metallic Fe by the gas product. Fe species （FeO and metallic Fe） acted as the active sites for tar catalytic decomposition. The catalytic temperature had a significant influence on tar conversion and the composition of the gas product. Typically, the tar conversion of about 17%-54% could be achieved when the catalytic temperature was varied from 750 to 950 ℃. Also, the content of H2 and CO increased with the catalytic temperature, while that of CO2, CH4 and C2Hx in the gas product decreased. It was demonstrated that copper slag was a good catalyst for upgrading the gas product from biomass gasification.

Dry reforming of methane on doped Ni nanoparticles:Featureassisted optimizations and ranking of doping metals for direct activations of CH_(4) and CO_(2)

As a vital energy resource and raw material for many industrial products,syngas(CO and H_(2))is of great significance.Dry reforming of methane(DRM)is an important approach to producing syngas(with a hydrogen-to-carbon-monoxide ratio of 1:1 in principle)from methane and carbon dioxide,with a lower operational cost as compared to other reforming techniques.However,many pure metallic catalysts used in DRM face deactivation issues due to coke formation or sintering of the metal particles.A systematic search for highly efficient metallic catalysts,which reduce the reaction barriers for the rate-determining steps and resist carbon deposition,is urgently needed.Nickel is a typical low-cost transition metal for activating the C–H bond in methane.In this work,we applied a two-step workflow to search for nickel-based bimetallic catalysts with doping metals M(M-Ni)by combining density functional theory(DFT)calculations and machine learning(ML).We focus on the two critical steps in DRM—CH_(4) and CO_(2) direct activations.We used DFT and slab models for the Ni(111)facet to explore the relevant reaction pathways and constructed a data set containing structural and energetic information for representative M-Ni systems.We used this dataset to train ML models with chemical-knowledge-based features and predicted CH_(4) and CO_(2) dissociation energies and barriers,which revealed the composition–activity relationships of the bimetallic catalysts.We also used these models to rank the predicted catalytic performance of candidate systems to demonstrate the applicability of ML for catalyst screening.We emphasized that ML ranking models would be more valuable than regression models in high-throughput screenings.Finally,we used our trained model to screen 12 unexplored M-Ni systems and showed that the DFT-computed energies and barriers are very close to the ML-predicted values for top candidates,validating the robustness of the trained model.