Bolshev, Vadim Yevgenyevich

Lansberg A.A., Bolshev V.Y., Panfilov A.A.

Tikhomirov D., Khimenko A., Kuzmichev A., Budnikov D., Bolshev V.

Drying food stuffs and other materials belongs to one of the most commonly used feedstock processing techniques, featuring rather high energy consumption. The major disadvantage of conventional electric convective-type household dryers is substantial thermal energy emission into the environment with a wet exhaust, worked-out drying agent. Among other principal disadvantages common to all dryers of this type, the following have to be mentioned: spatial inhomogeneity of heating a product under processing and that of drying agent distribution due to its temperature reduction and relative humidity growth as it moves upwards. A block diagram and a breadboard model of a convective-type thermoelectric dryer employing a thermoelectric heat pump have been designed. In our approach, a product is treated with the help of a drying agent (normally, heated air) with partial exhaust-air recirculation and heat recovery. Laboratory studies of the drying process have been carried out using apple fruits as a test material in order to evaluate the power consumed for evaporation of 1 kg of water in the newly developed convective-type thermoelectric drying unit. Physical parameters of apple fruits before and after drying both in the thermoelectric drying unit and in a conventional series-produced convective-type domestic dryer have been reported. The energy efficiency of the newly designed drying unit has been compared with that of some series-produced samples. It has been found out that, unlike conventional convective-type dryers, the breadboard model of the developed thermoelectric drying unit features a smoother product drying process owing to the presence of side air channels and more effective drying agent path organization in the processing chamber. This conclusion was supported by the results of the carried out tests. Application of thermoelectric heat pumps with the function of the exhaust drying agent heat recovery will make it possible to reduce the drying agent heater installed capacity and the power consumed by the newly designed convective-type thermoelectric drying unit by up to 20% in the course of the drying process, compared to series-produced household convective-type dryers.

Jasim A.M., Jasim B.H., Flah A., Bolshev V., Mihet-Popa L.

Demand Side Management (DSM) implies intelligently managing load appliances in a Smart Grid (SG). DSM programs help customers save money by reducing their electricity bills, minimizing the utility’s peak demand, and improving load factor. To achieve these goals, this paper proposes a new load shifting-based optimal DSM model for scheduling residential users’ appliances. The proposed system effectively handles the challenges raised in the literature regarding the absence of using recent, easy, and more robust optimization techniques, a comparison procedure with well-established ones, using Renewable Energy Resources (RERs), Renewable Energy Storage (RES), and adopting consumer comfort. This system uses recent algorithms called Virulence Optimization Algorithm (VOA) and Earth Worm Optimization Algorithm (EWOA) for optimally shifting the time slots of shiftable appliances. The system adopts RERs, RES, as well as utility grid energy for supplying load appliances. This system takes into account user preferences, timing factors for each appliance, and a pricing signal for relocating shiftable appliances to flatten the energy demand profile. In order to figure out how much electricity users will have to pay, a Time Of Use (TOU) dynamic pricing scheme has been used. Using MATLAB simulation environment, we have made effectiveness-based comparisons of the adopted optimization algorithms with the well-established meta-heuristics and evolutionary algorithms (Genetic Algorithm (GA), Cuckoo Search Optimization (CSO), and Binary Particle Swarm Optimization (BPSO) in order to determine the most efficient one. Without adopting RES, the results indicate that VOA outperforms the other algorithms. The VOA enables 59% minimization in Peak-to-Average Ratio (PAR) of consumption energy and is more robust than other competitors. By incorporating RES, the EWOA, alongside the VOA, provides less deviation and a lower PAR. The VOA saves 76.19% of PAR, and the EWOA saves 73.8%, followed by the BPSO, GA, and CSO, respectively. The electricity consumption using VOA and EWOA-based DSM cost 217 and 210 USD cents, respectively, whereas non-scheduled consumption costs 273 USD cents and scheduling based on BPSO, GA, and CSO costs 219, 220, and 222 USD cents.

Bolshev V., Vinogradova A.

Fomin I., Chernyshov V., Borodin M., Belikov R., Bolshev V., Lansberg A.A.

Singh N., Chakrabarti T., Chakrabarti P., Panchenko V., Budnikov D., Yudaev I., Bolshev V.

Thermal power plants use coal as a fuel to create electricity while wasting a significant amount of energy as heat. If the heat and power plants are combined and used in cogeneration systems, it is possible to reuse the waste heat and hence enhance the overall efficiency of the power plant. In order to minimize production costs while taking system constraints into account, it is important to find out the optimal operating point of power and heat for each unit. Combined heat and power production is now widely used to improve thermal efficiency, lower environmental emissions, and reduce power generation costs. In order to determine the best solutions to the combined heat and power economic dispatch problem, several traditional as well as innovative heuristic optimization approaches were employed. This study offers a thorough analysis of the use of heuristic optimization techniques for the solution of the combined heat and power economic dispatch problem. In this proposed work, the most well-known heuristic optimization methods are examined and used for the solution of various generating unit systems, such as 4, 7, 11, 24, 48, 84, and 96, taking into account various constraints. This study analyzes how various evolutionary approaches are performed for various test systems. The heuristic methodologies’ best outcomes for various case studies with restrictions are contrasted.

Meena M., Kumar H., Chaturvedi N.D., Kovalev A.A., Bolshev V., Kovalev D.A., Sarangi P.K., Chawade A., Rajput M.S., Vivekanand V., Panchenko V.

Gasification technology often requires the use of modeling approaches to incorporate several intermediate reactions in a complex nature. These traditional models are occasionally impractical and often challenging to bring reliable relations between performing parameters. Hence, this study outlined the solutions to overcome the challenges in modeling approaches. The use of machine learning (ML) methods is essential and a promising integration to add intelligent retrieval to traditional modeling approaches of gasification technology. Regarding this, this study charted applied ML-based artificial intelligence in the field of gasification research. This study includes a summary of applied ML algorithms, including neural network, support vector, decision tree, random forest, and gradient boosting, and their performance evaluations for gasification technologies.

Das A., Das S., Das N., Pandey P., Ingti B., Panchenko V., Bolshev V., Kovalev A., Pandey P.

Biogas production from waste materials has emerged as a promising avenue for sustainable energy generation, offering a dual benefit of waste management and renewable energy production. The selection and preparation of waste feedstocks, including agricultural residues, food waste, animal manure, and municipal solid wastes, are important for this process, while the microbial communities are majorly responsible for bioconversions. This review explores the role of complex microbial communities and their functions responsible for the anaerobic digestion of wastes. It covers the crucial physiological processes including hydrolysis, acidogenesis, acetogenesis, and methanogenesis, elucidating the microbial activities and metabolic pathways involved in the prospects of improving the efficiency of biogas production. This article further discusses the influence of recent progress in molecular techniques, including genomics, metagenomics, meta-transcriptomics, and stable isotope probing. These advancements have greatly improved our understanding of microbial communities and their capabilities of biogas production from waste materials. The integration of these techniques with process monitoring and control strategies has been elaborated to offer possibilities for optimizing biogas production and ensuring process stability. Microbial additives, co-digestion of diverse feedstocks, and process optimization through microbial community engineering have been discussed as effective approaches to enhance the efficiency of biogas production. This review also outlines the emerging trends and future prospects in microbial-based biogas production, including the utilization of synthetic biology tools for engineering novel microbial strains and consortia, harnessing microbiomes from extreme environments, and integrating biogas production with other biotechnological processes. While there are several reviews regarding the technical aspects of biogas production, this article stands out by offering up-to-date insights and recommendations for leveraging the potential of microbial communities, and their physiological roles for efficient biogas production. These insights emphasize the pivotal role of microbes in enhancing biogas production, ultimately contributing to the advancement of a sustainable and carbon-neutral future.

Hussain M.A., Hati A.S., Chakrabarti P., Hung B.T., Bolshev V., Panchenko V.

Classical model-free predictive current control (MFPCC) is a robust control technique for a two-level inverter-fed induction-motor drive, with advantages that consist of a simple concept, rapid response, simple implementation, and excellent performance. However, the classic finite-control-set MFPCC still exhibits a significant current ripple. This article presents a method to enhance performance using a combination of model-free predictive current control (MFPCC) and discrete-space vector modulation (DSVM). The MFPCC employs an ultralocal model with an extended-state observer (ESO) that does not consider motor parameters, therefore improving the control system’s reliability by eliminating the parameter dependency. The proposed method integrates DSVM, which divides a single sample period into N equal intervals and generates virtual vectors to reduce stator current ripple. It achieves the minimum cost-function value across the entire operating range of the induction-motor (IM) drive by selecting the optimal vector from a limited set of permissible voltage vectors. Using DSVM effectively reduces the total harmonic distortion (THD) without any detrimental effects during transients or steady states. Experimental studies validate the effectiveness and superiority of the suggested technique over the Finite-Control-Set (FCS) MFPCC, which only considers real voltage vectors in its computations.

Bolshev V., Yuferev L., Vinogradov A., Bukreev A.

Electricity supply as well as the provision of other forms of resources is one of the foundations of efficient agriculture. However, due to the reduction in the number of people living in rural settlements, there have been a large number of power lines with considerable lengths supplying small loads, hence resulting in an increase in power supply efficiency. A single-wire power transmission is an option for reducing the capital cost of power line construction by utilizing fewer conductors and fittings and lighter power transmission towers while lowering operational expenses. This paper considers the possible methods for single-wire energy transmission via the analysis of information sources such as Yandex and Google search engines; Scopus and Google Scholar scientific databases; and Cyber Leninka, eLIBRARY.ru, Elsevier, Springer, IEEE Xplore, and IGI Global electronic libraries. The conducted review revealed four alternatives: a single-wire earth return (SWER) system, a single-wire balanced line (B-Line), resonant wireless power transmission (SWPT) system, and a resonant single-wire power transmission system. The latter is of particular interest due to the lack of comprehensive and detailed information describing this technology, although it has distinct characteristics because of the peculiarities of the resonant mode of operation. The paper provides a comprehensive review of all existing published materials on the topic of “resonant systems for the transmission of electrical energy along a single wire”. The study covers the history of development and the structure of this system; describes its features, advantages, and the problems of using it; and the experience and fields of its application.

Bolshev V., Panchenko V., Sibirev A.

Nowadays, the expansion of people into intact primary areas has been observed alongside an increase in the area of land devoted to crops, pastures, etc [...]

Khanna A., Lamba B.Y., Jain S., Bolshev V., Budnikov D., Panchenko V., Smirnov A.

In the past couple of years, the world has come to realize the importance of renewable sources of energy and the disadvantages of excessive use of fossil fuels. Numerous studies have been conducted to implicate the benefits of artificial intelligence in areas of green energy production. Artificial intelligence (AI) and machine learning algorithms are believed to be the driving forces behind the fourth industrial revolution and possess capabilities for interpreting non-linear relationships that exist in complex problems. Sustainable biofuels are derived from renewable resources such as plants, crops, and waste materials other than food crops. Unlike traditional fossil fuels such as coal and oil, biofuels are considered to be more sustainable and environmentally friendly. The work discusses the transesterification of jatropha oil into biodiesel using KOH and NaOH as alkaline catalysts. This research aims to examine and optimize the nonlinear relationship between transesterification process parameters (molar ratio, temperature, reaction time, and catalyst concentration) and biodiesel properties. The methodology employed in this study utilizes AI and machine learning algorithms to predict biodiesel properties and improve the yield and quality of biodiesel. Deep neural networks, linear regression, polynomial regression, and K-nearest neighbors are the algorithms implemented for prediction purposes. The research comprehensively examines the impact of individual transesterification process parameters on biodiesel properties, including yield, viscosity, and density. Furthermore, this research introduces the use of genetic algorithms for optimizing biodiesel production. The genetic algorithm (GA) generates optimal values for transesterification process parameters based on the desired biodiesel properties, such as yield, viscosity, and density. The results section presents the transesterification process parameters required for obtaining 72%, 85%, and 98% biodiesel yields. By leveraging AI and machine learning, this research aims to enhance the efficiency and sustainability of biodiesel production processes.

Kuzmichev A., Khimenko A., Tikhomirov D., Budnikov D., Jasiński M., Bolshev V., Ignatkin I.

Recommendations on the selection of air curtains and the calculation of their parameters for livestock premises in cattle management farms are made. The air curtain functioning principle is analyzed from the air jet theory point of view. The block diagram and modular design of air curtains with a variable air jet direction vector and with controlled slit width are designed. Laboratory tests of the newly designed air curtain structure are performed in accordance with the microclimate requirements for the cattle management farm premises. Based on the experimental results, the major air curtain parameters are calculated for the range from 10° to 60° of angle α between the direction of the air jet outward from the air curtain slit and aperture plane, and for the air curtain slit width b0 in the range from 0.05 m to 0.15 m with the account of the wind speed Vw variations. Calculated values for amounts of energy that have to be consumed to ensure the required air jet velocity, in the output from the air curtain, and those for the quantity of thermal energy required to heat the air supplied to the air curtain, depending on the angle α and on the slit width b0, can be helpful for selecting the power capacity of both the air curtain fan and electric heater. A block diagram of the air curtain control for cattle management farm premises is designed, enabling automatic control of the airflow rate, the angle of the air jet output from the air curtain slit, and the temperature of the heated air supplied to the air curtain, considering particular climate conditions. According to the preliminary estimate, applications of the newly designed air curtain will make it possible to reduce the energy consumed to maintain the required microclimate conditions in cattle management premises by 10% to 15% in the cold period.

Abdrashitov A., Gavrilov A., Marfin E., Panchenko V., Kovalev A., Bolshev V., Karaeva J.

One of the most well-known methods of intensifying the process of anaerobic digestion is the pretreatment of raw materials. For the first time, the use of a jet-driven Helmholtz oscillator for biomass pretreatment is proposed. The design of the device is optimal for creating hydraulic cavitation; however, in this case, acoustic oscillations are generated in the system and resonance occurs. In this study, the optimal design of this device was determined for the subsequent design of a cavitation reactor. The diameter of the resonant chamber was varied in the range from 28.3 to 47.5 mm, and its length from 6 to 14 mm; in addition, the diameter of the outlet was changed from 6.1 to 6.3 mm. Based on the experimental data obtained, it was found that the optimal ratio of the length of the resonator chamber to the diameter of the inlet nozzle is 1.73, and the inner diameter of the resonator chamber to the diameter of the inlet nozzle corresponds to 5.5. Improving the technology of agricultural waste disposal will ensure their maximum involvement in economic circulation, reduce the consumption of traditional fuel and energy resources, and improve the technological and machine-building base, which makes it possible to produce competitive cavitation reactors.

Yudaev I., Daus Y., Panchenko V., Bolshev V.

Due to the emerging danger to the life of animals and people, today there is a turn to safe technologies for controlling weeds by physical methods, both from the point of view of ecology and food safety, which include the destruction of plants using an electric current, in particular, high-voltage electrical pulses. The purpose of the study presented in the article is to identify and evaluate the effect of high-voltage electrical pulses on the irreversible damage to the intracellular structures of the plant tissue of weeds and unwanted grasses during their electric weed control, characterizing and evaluating the parameters and modes associated with such processing. Experimental studies were carried out using a laboratory experimental setup that consists of a pulse voltage generator, a control circuit for a spherical forming spark gap, and schemes for measuring the electrical resistance of the plant tissue of the weed sample. The lesion level made it possible to control the depth of irreversible damage to the internal structure of the plant tissue of weeds by measuring its tolerance (the conductivity of the tissue increased with increasing damage to the cellular components of the tissue).The irreversible damage to the plant tissues of weeds for weeds of various biological groups, which is characterized by reaching the value of at least 4.0–7.5 degrees of damage to their tissues, can be acted on them with high-voltage electrical pulses in the treated tissue of an electric field intensity of at least 3.74 kV/cm, while ensuring specific processing electric energy for the reliable processing of weeds: for Euphórbia virgáta, thise quals 5.2…17.5 J/cm3; for Amaránthus retrofléxus, it is 3.5…7.7 J/cm3; for Cirsium arvense, it is 2.7…10.9 J/cm3;for Sónchus arvénsis, it is 3.7…15.8 J/cm3; and for Lactúca tatárica, it is 3.3…8.1 J/cm3.

Pawuś D., Porażko T., Paszkiel S.

Escámez A., Aguado R., Sánchez-Lozano D., Jurado F., Vera D.

Ashokkumar V., Kumar G., Lakshmanan H., Chandramughi V.P., Flora G., Kothari R., Piechota G.

CHERIAN P., NITHANIYAL S., SRINIVASAN P.

Mpitsi M.C.

Chithra S., Arunachalaperumal C., Rajagopal R., Meenalochini P.

Increasing reliance on renewable energy sources (RES) within smart grid systems, ensuring power balance amid fluctuations in energy production and load demand presents a significant challenge. This study proposes a novel hybrid approach, termed the GJO-THDCNN technique, which integrates Golden Jackal Optimization (GJO) with a Tree Hierarchical Deep Convolutional Neural Network (THDCNN) to address this issue effectively. The proposed approach uses advanced controllers and power electronic converters to improve overall performance while integrating battery storage with solar and wind energy conversion systems. GJO generates optimized control signals, while the THDCNN enhances prediction accuracy by considering power demand, state-of-charge (SoC), and RES availability. Implemented in MATLAB, the model showcases superior performance compared to existing methods, achieving a remarkable 20% improvement in power output stability and a 30% reduction in response time to load variations. These findings underscore the GJO-THDCNN technique's potential for advancing energy management strategies in smart grids.

Dehghani M., Bornapour S.M.

Rao S.P., Olusegun T.S., Ranganathan P., Kose U., Goveas N.

Merta I., Serjun V.Z., Pranjić A.M., Šajna A., Štefančič M., Poletanović B., Ameri F., Mladenović A.

Rao R.V., Reddy U.S.

Sartoni M., Semercioz Oduncuoglu A.S., Guidi A., Annosi M.C., Luning P.A.

Shammi S.A., Ghosh P., Sutradhar A., Shamrat F.M., Moni M.A., Oliveira T.E.

Tozlu B.H.

Maurya P., Prasad D., Singh R.

ABSTRACTControl of a jacketed continuous stirred tank reactor (CSTR) is challenging due to nonlinear dynamics, complexity, and rapid reactor dynamics under imperfect mixing in the jacket. Current controller designs mainly focus on the two‐state model, neglecting the potential of three‐state models in scenarios with nonperfect mixing and fast reactor dynamics. This study proposes a sliding mode controller (SMC) design scheme based on the transfer function model using a newly developed jellyfish optimisation algorithm. Further, a fractional‐order sliding mode control (FO‐SMC) strategy is proposed, which integrates modifications to the SMC to mitigate chattering, enhance control robustness, and provide better disturbance rejection capability. PID and fractional‐order PID (FOPID) controllers were also designed for comparative analysis. The simulation results demonstrated that FO‐SMC outperformed other designed controllers, shown by a 37.14% reduction in settling time, 10.69% reduction in integral absolute error (IAE), and 19.06% reduction in time‐weighted absolute error (ITAE) compared to SMC and various other improved performance indicators. Parameter variation and noise analysis highlighted the ability of the controller to maintain stability and performance under dynamic conditions.

Baviskar P.V., Nemade V.A., Mahale V.V.

Chronic Kidney Disease (CKD) poses a substantial global health challenge, characterized by elevated morbidity and mortality rates and the potential to trigger cascading health issues. The mild beginning of CKD, characterized by the absence of overt symptoms in the early stages, frequently results in a lack of awareness among patients. Timely detection of CKD is crucial for providing patients with prompt interventions to mitigate disease progression. In this paper, we have analyzed performance of six different algorithms (J48, Random forest, Naive Bayes, Support Vector Machine, MLP, and K-Nearest Neighbor) on the CKD dataset from Kaggle.com and provided a performance analysis using the evaluation parameters. A thorough review of the literature on machine learning models for CKD early detection is also provided in this research. We categorize and examine several machine learning approaches, including algorithms, datasets, and assessment metrics used in CKD prediction, after conducting a thorough analysis of the current literature. Our study reveals essential techniques and outcomes, providing insight into the complexities of current strategy. We address the clinical implications of these findings and identify potential areas for future investigation. The results obtained on the six algorithms shows that SVM has achieved the highest accuracy of 97%.

George M.A., Kamat D.V., Kurian C.P.

Electric vehicles (EVs) have assumed prominence due to their enhanced performance, efficiency, and zero carbon emission. This paper proposes an efficient adaptive neuro-fuzzy inference system (ANFIS) based fractional order PID (FOPID) controller for an EV speed tracking control driven by a DC motor. The optimal controller parameters of the FOPID controller are found via an Ant Colony Optimization (ACO) method. The ANFIS controllers are well trained, tested, and validated using the data set sextracted from the fuzzy-based controllers. The performance and accuracy of the ANFIS model are evaluated using statistical parameters such as mean square error (MSE), coefficient of correlation (R), and root mean square error (RMSE). The controller performance, energy consumption, and robustness are tested using the new European drive cycle (NEDC) test. The efficacy of the ANFIS-based controller is demonstrated by comparing its performance with properly tuned fuzzy-based controllers. The proposed controller shows robustness towards external disturbances and offers promising EV speed regulation control. The comparative results illustrate the superior performance of ANFIS-based FOPID controller with high prediction and low error rates. MATLAB- Simulink platform is used for system modeling, controller design, and numerical simulation.

Chen Z., Kuhn D., Wiesemann W.

In the era of modern business analytics, data-driven optimization has emerged as a popular modeling paradigm to transform data into decisions. By constructing an ambiguity set of the potential data-generating distributions and subsequently hedging against all member distributions within this ambiguity set, data-driven optimization effectively combats the ambiguity with which real-life data sets are plagued. Chen et al. (2022) study data-driven, chance-constrained programs in which a decision has to be feasible with high probability under every distribution within a Wasserstein ball centered at the empirical distribution. The authors show that the problem admits an exact deterministic reformulation as a mixed-integer conic program and demonstrate (in numerical experiments) that the reformulation compares favorably to several state-of-the-art data-driven optimization schemes.

Ayub Y., Hu Y., Ren J.

Quality syngas production with higher moles of hydrogen and methane are the primary objective of gasification process which is dependent upon the process parameters and composition of biomass. However, it is always a costly and time-consuming task to get the optimum biomass composition and process parameters for quality syngas production. In this research, artificial intelligence (AI) algorithms have been applied for high quality syngas prediction with better moles fractions of hydrogen and methane using hydrothermal gasification (HTG). Comparative analysis of Convolutional Neural Network (CNN), Artificial Neural Network (ANN), Gradient Boost Regressor (GBR), Extreme Boost Regressor (XGB), and Random Forest Regressor (RFR) based algorithms have been done to select an optimal one. Ultimate analysis of biomass and process input parameters inlcuding temperature, pressure, percentage solid content of biomass, and resident time have been used as an input parameter for prediction models. Final comparative results of these AI models conclude that XGB has a better prediction result as compared to other with coefficient of determinant (R2) and mean square errors ranges from 0.85 to 0.95 and 0.008–0.01, respectively. Furthermore, process temperature and the resident time are the most contributing factors in mole fractions of hydrogen and methane. Higher hydrogen and oxygen contents in the biomass, significantly contributes to the production of quality syngas.

Kim J.Y., Shin U.H., Kim K.

A fluidized bed system for biomass gasification is considered a useful implementation with advantages of good heat and mass transfer and uniform heat across the bed that can reduce local hot spot which produces heavy hydrocarbons including tar. Such a system has been studied for decades for product conversion and selectivity optimization with empirical correlations, mathematical modelling, and machine learning. Although machine learning has been recently adopted for predicting biomass composition and operating conditions, the number of algorithms chosen from previous research is limited due to various types of models and complexity. An automated machine learning (AutoML) was adopted here to select the best machine learning algorithm amongst various types of models including tree ensembles and neural networks. Using AutoML, operating conditions and lignocellulosic compositions were predicted with output features from the system, including syngas composition, LHV, char yield, and tar yield. Generally, CatBoost (gradient boosting on decision trees) algorithm showed a good match with experimental data results/test data with high R2 (0.689) and low RMSE (0.220). Combined cooperative game theory (Shapely additive explanation, SHAP) was also applied to develop an interpretable model. AutoML combined with the SHAP algorithm for explainable machine learning was first tried in the field of fluidized bed systems to find a suitable machine learning model for each feature with hyperparameters optimization and expected to help in interpreting the results with limited results that require exhaustive experimentation. The results can be widely adapted in various scenarios, such as monitoring the process as a soft sensor.

Yang Y., Shahbeik H., Shafizadeh A., Rafiee S., Hafezi A., Du X., Pan J., Tabatabaei M., Aghbashlo M.

The gasification process can treat and valorize municipal solid waste (MSW) in an environmentally and economically friendly way. Using this process, MSW can be safely disposed of and sustainably converted into bioenergy as part of regional planning. Experimental laboratory data is a key component in designing, optimizing, controlling, and scaling up MSW gasifiers. However, most researchers lack the resources and time to conduct experiments. Machine learning (ML) technology can resolve this issue by detecting patterns and hidden information in published data. Hence, the present study aims to construct an inclusive ML model to predict and understand the MSW gasification process. The objective is to establish a consistent and homogeneous database containing MSW sources under different gasification conditions, followed by an analysis of the database using statistical methods. Three ML models are used to predict the distribution of syngas, char, and tar and the quality of syngas in MSW gasification using feedstock characteristics and gasification parameters. When a gradient boost regressor is used to model the process, the prediction accuracy is highest (R2 > 0.926, RMSE

Zhao S., Zhang Y., Xu W., Gu H.

Tar problem is an obstacle in biomass gasification. Naphthalene was used as tar model compound to investigate the tar catalytic removal over char bed coupled with hydrogen production. The influence of char properties, residence time and atmosphere on tar reduction was taken into consideration. Results show pinewood char acted as quite good performance for catalytic removal of naphthalene. With the increasing of duration time, deactivation would occur, which brought down the tar conversion efficiency. The addition of H2O could inhibit the carbon deposition and promote hydrogen yield via in-situ gasification. At 800 °C, the naphthalene conversion rate slightly declined to 95.77% even at 182 min under 10% steam atmosphere. The H2 yield was around 8.98 mol/(mole of naphthalene) at initial 12 min. The pinewood char pore analysis results confirmed the inhibition of carbon deposition by steam. Artificial neural network (ANN) models coupled with genetic algorithm (GA) and particle swarm optimization (PSO) were built for prediction of naphthalene conversion and hydrogen yield. The BET surface area, potassium content, penetration time, duration time, temperature and atmosphere were used as input variables. Modeling results show that the PSO-ANN model and relative impact analysis could be effectively used for modeling and analysis of tar catalytic conversion over char bed.

Singh A.P., Gupta A., Biswas A., Das B.

Yang Q., Zhang J., Zhou J., Zhao L., Zhang D.

Gasification technology can effectively improve the utilization efficiency of coal and biomass resources. However, conventional experimental methods are costly, time-consuming, and labor-intensive to optimize the system performance of the different coal or biomass gasification process. Therefore, this study developed a hybrid data-driven machine learning framework for predicting the performance of coal and biomass gasification processes. To select the best machine learning model for the gasification process, the artificial neural network (ANN), decision tree, multiple linear regression, and support vector machine models are established with the hybrid database and assessed by seven regression evaluation indicators. The results indicate ANN model has the best prediction performance because it has the highest coefficient of determination (0.9242). To improve the prediction accuracy of the ANN model, the number of its hidden layers and neurons is first investigated and optimized. The results indicate that the preferred network structure of the ANN model is a double hidden layer neural network with 24 neurons. A genetic algorithm is then employed to improve the prediction performance of the optimized ANN model, which can further reduce the error of the ANN model. Finally, the genetic algorithm-optimized ANN model is applied to analyze the actual coal and biomass gasification processes. Results show that anthracite coal mixed with pine sawdust has the most significant impact on the gas yield of the gasification process, and bituminous coal mixed with rice husk has the most significant impact on the lower heating value of gasification process. Although the model has good predictive performance, it can continue to be improved by considering different equivalence or gasification ratios.

Hai T., Ashraf Ali M., Alizadeh A., Zhou J., Dhahad H.A., Kumar Singh P., Fahad Almojil S., Ibrahim Almohana A., Fahmi Alali A., Shamseldin M.

The current research study focuses on modeling solid oxide fuel cell (SOFC) power plants. For this purpose, in the research, three Integrated processes are presented to achieve the most optimal system from the perspective of energy and economics. An integrated SOFC is considered in the first model, the second model is focused on using the wasted heat from the first model as the entry of the Stirling engine, and in the third model, the excess energy of the Stirling engine is used to produce hydrogen with the help of proton exchange membrane electrolyze and also power generated by the first model turbine is used in desalination system to produce fresh water. Power generation and hydrogen production from the systems are considered the main two objective functions. Results show that in the presented system the most optimal state of energy efficiency is 39.6% and with an economic cost of 10.30 dollars per hour. The results also indicate that the presented energy system can produce 191 kW of output power, and 23 kg/s of hydrogen fuel with an economic cost of nearly 11 dollars/hour at its working point.

Singh N., Chakrabarti T., Chakrabarti P., Margala M., Gupta A., Krishnan S.B., Unhelkar B.

Most power is generated using fossil fuels like coal, natural gas, and diesel. The contribution of coal to power generation is very high compared to other sources. Almost all thermal power plants use coal as a fuel for power generation. Such sources of fossil fuels are limited and thus the cost of power generation increases. At the same time, the induced toxic gases due to these fossil fuels pollute the environment. The objective of this work is to solve the economic emission dispatch problem. Economic emission dispatch helps to find out how to operate power plants at the minimum cost and induce the minimum emissions at a thermal power plant. Economic emission dispatch with constraints is a nonlinear optimization problem. For the solution of such nonlinear economic emission load dispatch problems, this work considers a new particle swarm optimization technique. The proposed new PSO gives the best solution for economic emission load dispatch and handles the constraints. For the testing of the proposed new PSO algorithm, this work considered a case study of a system of six generating units, and it was tested for load demands of 700 MW, 800 MW, and 1000 MW. The results of the new PSO for the three load demands considered give the minimum generation cost, minimum emission, and minimum total cost compared to other optimization algorithms. The proposed techniques are effective, and they can help obtain the minimum generation cost and minimize emissions.

Singh N., Chakrabarti T., Chakrabarti P., Margala M., Gupta A., Praveen S.P., Krishnan S.B., Unhelkar B.

The fundamental objective of economic load dispatch is to operate the available generating units such that the needed load demand satisfies the lowest generation cost and also complies with the various constraints. With proper power system operation planning using optimized generation limits, it is possible to reduce the cost of power generation. To fulfill the needs of such objectives, proper planning and economic load dispatch can help to plan the operation of the electrical power system. To optimize the economic load dispatch problems, various classical and new evolutionary optimization approaches have been used in research articles. Classical optimization techniques are outdated due to many limitations and are also unable to provide a global solution to the ELD problem. This work uses a new variant of particle swarm optimization techniques called modified particle swarm optimization, which is effective and efficient at finding optimum solutions for single as well as multi-objective economic load dispatch problems. The proposed MPSO is used to solve single and multi-objective problems. This work considers constraints like power balance and power generation limits. The proposed techniques are tested for three different case studies of ELD and EELD problems. (1) The first case is tested using the data of 13 generating unit systems along with the valve point loading effect; (2) the second case is tested using 15 generating unit systems along with the ramp rate limits; and (3) the third case is tested using the economic emission dispatch (EELD) as a multi-objective problem for 6 generating unit systems. The outcomes of the suggested procedures are contrasted with those of alternative optimization methods. The results show that the suggested strategy is efficient and produces superior optimization outcomes than existing optimization techniques.

Leca E., Zennaro B., Hamelin J., Carrère H., Sambusiti C.

Nowadays, anaerobic digestion (AD) is being increasingly encouraged to increase the production of biogas and thus of biomethane. Due to the high diversity among feedstocks used, the variability of operating parameters and the size of collective biogas plants, different incidents and limitations may occur (e.g., inhibitions, foaming, complex rheology). To improve performance and overcome these limitations, several additives can be used. This literature review aims to summarize the effects of the addition of various additives in co-digestion continuous or semi-continuous reactors to fit as much as possible with collective biogas plant challenges. The addition of (i) microbial strains or consortia, (ii) enzymes and (iii) inorganic additives (trace elements, carbon-based materials) in digester is analyzed and discussed. Several challenges associated with the use of additives for AD process at collective biogas plant scale requiring further research work are highlighted: elucidation of mechanisms, dosage and combination of additives, environmental assessment, economic feasibility, etc.

Shepovalova O., Izmailov A., Lobachevsky Y., Dorokhov A.

Developing an energy supply based on resources whose use does not spoil the noosphere and the creation of such energy supply of efficient equipment whose operation does not cause any damage to nature and man is an urgent task. The need for such an approach is especially relevant and noticeable in agriculture. This article presents the final results of complex studies of new PV devices and PV systems based on them. Considered in the article are the best solutions we propose to improve PV equipment and make it more attractive for agricultural consumers. The developed vertical and planar high-voltage multijunction silicon PV cells and PV modules on their basis are presented. The first type of modules have a maximum power point voltage of up to 1000 V, specific power of up to 0.245 ± 0.01 W/cm2, and efficiency of up to 25.3% under a concentration ratio range of 10–100 suns. The samples of the second module type (60,156.75 × 156.75 mm PV cells) have an open-circuit voltage of 439.7 V, a short-circuit current of 0.933 A, and a maximum power of 348 W. Additionally, two types of newly designed solar energy concentrators are described in this article: one-dimensional double-wing concentrator ensuring low Fresnel optical losses and multi-zone parabolotoric microconcentrator with the uniform radiation distribution in the focal region, as well as modules based on these concentrators and the developed PV cells. For PV modules, the maximum power degradation is 0.2–0.24% per year in a wet ammonia environment. For concentrating PV modules, this degradation is 0.22–0.37% per year. This article sets out the principles of increasing the efficiency of PV systems by increasing the level of systematization and expanding the boundaries of PV systems. The thus-created PV systems satisfy 30–50% more consumer needs. Thanks to a higher output voltage and other specific features of the developed modules, PV system loss decreased by 12–15%, and maintenance losses also decreased.

Cahanap D.R., Mohammadpour J., Jalalifar S., Mehrjoo H., Norouzi-Apourvari S., Salehi F.

Further efforts are still needed to refine and optimise complex thermochemical pyrolysis processes crucial in waste management and clean energy production. In this work, a comparative artificial intelligence (AI) based modelling study is conducted using four supervised machine learning models, including artificial neural network (ANN), random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGB) to predict the three-phase product yields of pyrolysis. The models were trained using a database of previous experiments focused on continuous pyrolysis in fluidised bed reactors, with biomass feedstock characteristics and pyrolysis conditions as input features. A reactor dimension parameter through H/D (the ratio of the reactor height, H and the reactor diameter, D), for the first time, is also included as an input feature. The models are optimised through feature reduction and 5-fold cross-validation hyperparameter tuning. They show that reducing the organic composition of biomass to include only chemical composition results in the best feature-reduced model. After the comparison of performance scores and total feature importance, the general ranking for AI model accuracy for this study is XGB>RF>ANN>SVR. The H/D ratio also has the highest feature importance scores of 21.71% and 29.52% in predicting the oil and gas yield of the feature-reduced XGB model, confirming the importance of this added parameter. Preliminary contour plot analysis of the database shows that for the considered reactors, optimum oil yields are obtained at H/D ratio< 5, while the optimum gas yields are expected at H/D ratioc closer to 10 for fluidised bed reactors as another indicator of factor importance.

Hai T., El-Shafay A.S., Alizadeh A., Almojil S.F., Almohana A.I., Alali A.F.

Integrating heat recovery units into modern power plants is a good way to improve their efficiency in the future. To address this challenge, this study aims to design and develop an electricity/cooling cogeneration system (ECCS) that uses a solid oxide fuel cell (SOFC), fed by CH4, along with a booster/ejector-assisted organic flash cycle that recovers heat from the exhausted gasses. An extended evaluation of the output of the proposed ECCS is conducted based on energy-exergy, environmental, and exergoeconomic criteria, in which the SOFC's current density (JSOFC), the SOFC operating temperature (TSOFC), along with the flashing tank temperature (TFT), are considered variables. Accordingly, the exergetic efficiency and cost of products as objective functions are optimized by the NSGA-II method. The optimum state is reachable at TFT=370 K, TSOFC=895 K, and JSOFC=5000 A/m2. Here, the exergetic efficiency and cost of products are calculated at 53.23% and 43.27 $/GJ. Accordingly, energetic efficiency and total exergoeconomic factor are enhanced by 1.33% and 1.96%, respectively, compared with the based case. Moreover, a variation in TFT does not have any effect on the pollution damage cost (PDC) at all, in fact, the PDC remains at 3.066 $/h regardless of the variation in TFT.