FEM-based study of precision hard turning of stainless steel 316L

This study aims to investigate chip formation and surface generation during the precision turning of stainless steel 316L samples. A Finite Element Method (FEM) was used to simulate the chipping process of the stainless steel but with only a restricted number of process parameters. A set of turning tests was carried out using tungsten carbide tools under similar cutting conditions to validate the results obtained from the FEM for the chipping process and at the same time to experimentally examine the generated surface roughness. These results helped in the analysis and understanding the chip formation process and the surface generation phenomena during the cutting process, especially on micro scale. Good agreement between experiments and FEM results was found, which confirmed that the cutting process was accurately simulated by the FEM and allowed the identification of the optimum process parameters to ensure high performance. Results obtained from the simulation revealed that, an applied feed equals to 0.75 of edge radius of new cutting tool is the optimal cutting conditions for stainless steel 316L. Moreover, the experimental results demonstrated that in contrast to conventional turning processes, a nonlinear relationship was found between the feed rate and obtainable surface roughness, with a minimum surface roughness obtained when the feed rate laid between 0.75 and 1.25 times the original cutting edge radius, for new and worn tools, respectively

Fuzzy modeling and parameters optimization for the enhancement of biodiesel production from waste frying oil over montmorillonite clay K-30

Transesterification is a promising technology for the biodiesel production to provide an alternative fuel that considers the environmental concerns. From the economic and environmental protection points of view, utilization of waste frying oil for the production of biodiesel addresses very beneficial impacts. Production of higher yield of biodiesel is a challenging process in order to commercialize it with a lower cost. The current study focuses on the influence of different parameters such as reaction temperature (°C), reaction period (min), oil to methanol ratio and amount of catalyst (wt%) on the production of biodiesel. The main objective of this work is to develop a model via fuzzy logic approach in order to maximize the biodiesel produced from waste frying oil using montmorillonite Clay K-30 as a catalyst. The optimization for the operating parameters has been performed via particle swarm optimization (PSO) approach. During the optimization process, the decision variables were represented by four different operating parameters: temperature (40–140 °C), reaction period (60–300 min), oil/methanol ratio (1:6–1:18) and amount of catalyst (1–5 wt%). The model has been validated with the experimental data and compared with the optimal results reported based on other optimization techniques. Results showed the increment of biodiesel production by 15% using the proposed strategy compared to the earlier study. The obtained biodiesel production yield reached 93.70% with the optimal parameters for a temperature at 69.66 °C, a reaction period of 300 min, oil/methanol ratio of 1:9 and an amount of catalyst of 5 wt%

Optimizing density, dynamic viscosity, thermal conductivity and specific heat of a hybrid nanofluid obtained experimentally via ANFIS-based model and modern optimization

In this study, rGO/Co3O4 nanocomposite was synthesized, characterized, and then the thermophysical properties were obtained experimentally, after which the experimental data at varying values of temperature and particle loadings was used for optimization purposes. The study was concerned with different values of the controlling parameters. The in-situ/chemical reduction technique was used to synthesize the rGO/Co3O4 nanocomposite and then characterized with x-ray diffraction, transmission electron microscope, and magnetometry. The system was studied at temperature values ranging at 20, 30, 40, 50, and 60 °C and with particle loadings of 0.05%, 0.1%, and 0.2% wt%. The authors in this article have introduced a novel population-based algorithm that is known as Marine Predators Algorithm to obtain the optimal values of the controlling parameters (i.e., temperature and nanofluid mixture percentage) that minimize two controlled variables (i.e., density and viscosity) as well as maximize the other two controlled variables (thermal conductivity and specific heat). The rGO/Co3O4 nanocomposite nanofluid thermal conductivity and viscosity were investigated experimentally, and a maximum increment of 19.14% and 70.83% with 0.2% particle loadings at 60 °C was obtained. At 0.05%, 0.1%, and 0.2% particle loading wt%, the density increased by 0.115%, 0.23%, and 0.451% at a temperature of 20 °C; simultaneously, density increased by 0.117%%, 0.235%, and 0.469% at 60 °C, respectively as compared to water. At 0.2 wt%, the maximum decreased specific heat was 0.192% and 0.194% at 20 °C and 60 °C. When compared with water, no effect was observed with an increase in temperature/: a similar trend as that of the water was followed. The optimal values were found to be at a temperature of 60 °C and for 0.05% particle loading of the prepared nanofluid. However, among the conducted experiments, the optimizer pointed out that the optimal experiment was the one conducted at a temperature of 60 °C and a nanofluid percentage at 0.05. In conclusion, the proposed methodology of modelling with an artificial intelligence tool such as an adaptive network-based fuzzy inference system technique and then determining the optimal parameters with the marine predators algorithm accomplished the goal of the study with major success.publishe

Review of Metaheuristic Optimization Algorithms for Power Systems Problems

Metaheuristic optimization algorithms are tools based on mathematical concepts that are used to solve complicated optimization issues. These algorithms are intended to locate or develop a sufficiently good solution to an optimization issue, particularly when information is sparse or inaccurate or computer capability is restricted. Power systems play a crucial role in promoting environmental sustainability by reducing greenhouse gas emissions and supporting renewable energy sources. Using metaheuristics to optimize the performance of modern power systems is an attractive topic. This research paper investigates the applicability of several metaheuristic optimization algorithms to power system challenges. Firstly, this paper reviews the fundamental concepts of metaheuristic optimization algorithms. Then, six problems regarding the power systems are presented and discussed. These problems are optimizing the power flow in transmission and distribution networks, optimizing the reactive power dispatching, optimizing the combined economic and emission dispatching, optimal Volt/Var controlling in the distribution power systems, and optimizing the size and placement of DGs. A list of several used metaheuristic optimization algorithms is presented and discussed. The relevant results approved the ability of the metaheuristic optimization algorithm to solve the power system problems effectively. This, in particular, explains their wide deployment in this field

Metaheuristic-Based Algorithms for Optimizing Fractional-Order Controllers—A Recent, Systematic, and Comprehensive Review

Metaheuristic optimization algorithms (MHA) play a significant role in obtaining the best (optimal) values of the system’s parameters to improve its performance. This role is significantly apparent when dealing with systems where the classical analytical methods fail. Fractional-order (FO) systems have not yet shown an easy procedure to deal with the determination of their optimal parameters through traditional methods. In this paper, a recent, systematic. And comprehensive review is presented to highlight the role of MHA in obtaining the best set of gains and orders for FO controllers. The systematic review starts by exploring the most relevant publications related to the MHA and the FO controllers. The study is focused on the most popular controllers such as the FO-PI, FO-PID, FO Type-1 fuzzy-PID, and FO Type-2 fuzzy-PID. The time domain is restricted in the articles published through the last decade (2014:2023) in the most reputed databases such as Scopus, Web of Science, Science Direct, and Google Scholar. The identified number of papers, from the entire databases, has reached 850 articles. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was applied to the initial set of articles to be screened and filtered to end up with a final list that contains 82 articles. Then, a thorough and comprehensive study was applied to the final list. The results showed that Particle Swarm Optimization (PSO) is the most attractive optimizer to the researchers to be used in the optimal parameters identification of the FO controllers as it attains about 25% of the published papers. In addition, the papers that used PSO as an optimizer have gained a high citation number despite the fact that the Chaotic Atom Search Optimization (ChASO) is the highest one, but it is used only once. Furthermore, the Integral of the Time-Weighted Absolute Error (ITAE) is the best nominated cost function. Based on our comprehensive literature review, this appears to be the first review paper that systematically and comprehensively addresses the optimization of the parameters of the fractional-order PI, PID, Type-1, and Type-2 fuzzy controllers with the use of MHAs. Therefore, the work in this paper can be used as a guide for researchers who are interested in working in this field

Improving CO₂ Absorption Using Artificial Intelligence and Modern Optimization for a Sustainable Environment

One of the essential factors in maintaining environmental sustainability is to reduce the harmful effects of carbon dioxide (CO2) emissions. This can be performed either by reducing the emissions themselves or capturing and storing the emitted CO2. This work studies the solubility of carbon dioxide in the capturing solvent, which plays a crucial role in the effectiveness and cost-efficiency of carbon capture and storage (CCS). Therefore, the study aims to enhance the solubility of CO2 by integrating artificial intelligence (AI) and modern optimization. Accordingly, this study consists of two consecutive stages. In the first stage, an adaptive neuro-fuzzy inference system (ANFIS) model as an AI tool was developed based on experimental data. The mol fraction was targeted as the model’s output in terms of three operating parameters; the concentration of tetrabutylphosphonium methanesulfonate [TBP][MeSO3], temperature, and pressure of CO2. The operating ranges are (2–20 wt%), (30–60 °C), and (2–30 bar), respectively. Based on the statistical measures of the root mean squared error (RMSE) and the predicted R2, the ANFIS model outperforms the traditional analysis of variance (ANOVA) modeling technique, where the resulting values were found to be 0.126 and 0.9758 for the entire samples, respectively. In the second stage, an improved grey wolf optimizer (IGWO) was utilized to determine the optimal operating parameters that increase the solubility of CO2. The optimal values of the three operating parameters that improve the CO2 solubility were found to be 3.0933 wt%, 40.5 °C, and 30 bar, respectively. With these optimal values, the collaboration between the ANFIS and IGWO produced an increase of 13.4% in the mol fraction compared to the experimental data and the response surface methodology. To demonstrate the efficacy of IGWO, the obtained results were compared to the results of four competitive optimization techniques. The comparison showed that the IGWO demonstrates superior performance. Overall, this study provided a cost-efficient approach based on AI and modern optimization to enhance CO2 solubility in CCS

Parameter Estimation-Based Slime Mold Algorithm of Photocatalytic Methane Reforming Process for Hydrogen Production

The key contribution of this paper is to determine the optimal operating parameters of the methane reforming process for hydrogen production. The proposed strategy contained two phases: ANFIS modelling and optimization. Four input controlling parameters were considered to increase the hydrogen: irradiation time (min), metal loading, methane concentration, and steam concentration. In the first phase, an ANFIS model was created with the help of the experimental data samples. The subtractive clustering (SC) technique was used to generate the fuzzy rules. In addition, the Gaussian-type and weighed average were used for the fuzzification and defuzzification methods, respectively. The reliability of the resulting model was assessed statistically by RMSE and the correlation (R2) measures. The small RMSE value and high R2 value of testing samples assured the correctness of the modelling phase, as they reached 0.0668 and 0.981, respectively. Based on the robust model, the optimization phase was applied. The slime mold algorithm (SMA), as a recent as well as simple optimizer, was applied to look for the best set of parameters that maximizes hydrogen production. The resulting values were compared by the findings of three competitive optimizers, namely particle swarm optimization (PSO), Harris hawks optimization (HHO), and evolutionary strategy HHO (EESHHO). By running the optimizers 30 times, the statistical results showed that the SMA obtained the maximum value with high mean, standard deviation, and median. Furthermore, the proposed strategy of combining the ANFIS modelling and the SMA optimizer produced an increase in the hydrogen production by 15.7% in comparison to both the experimental and traditional RSM techniques

Parameter Estimation-Based Slime Mold Algorithm of Photocatalytic Methane Reforming Process for Hydrogen Production

The key contribution of this paper is to determine the optimal operating parameters of the methane reforming process for hydrogen production. The proposed strategy contained two phases: ANFIS modelling and optimization. Four input controlling parameters were considered to increase the hydrogen: irradiation time (min), metal loading, methane concentration, and steam concentration. In the first phase, an ANFIS model was created with the help of the experimental data samples. The subtractive clustering (SC) technique was used to generate the fuzzy rules. In addition, the Gaussian-type and weighed average were used for the fuzzification and defuzzification methods, respectively. The reliability of the resulting model was assessed statistically by RMSE and the correlation (R2) measures. The small RMSE value and high R2 value of testing samples assured the correctness of the modelling phase, as they reached 0.0668 and 0.981, respectively. Based on the robust model, the optimization phase was applied. The slime mold algorithm (SMA), as a recent as well as simple optimizer, was applied to look for the best set of parameters that maximizes hydrogen production. The resulting values were compared by the findings of three competitive optimizers, namely particle swarm optimization (PSO), Harris hawks optimization (HHO), and evolutionary strategy HHO (EESHHO). By running the optimizers 30 times, the statistical results showed that the SMA obtained the maximum value with high mean, standard deviation, and median. Furthermore, the proposed strategy of combining the ANFIS modelling and the SMA optimizer produced an increase in the hydrogen production by 15.7% in comparison to both the experimental and traditional RSM techniques

An Effective Energy Management Strategy Based on Mine-Blast Optimization Technique Applied to Hybrid PEMFC/Supercapacitor/Batteries System

An effective energy management strategy based on the mine-blast optimization (MBA) technique was proposed in this paper to optimally manage the energy in a hybrid power system. The hybrid system was composed of fuel cells, batteries, and supercapacitors. Such system was employed to supply highly fluctuated load. The results of the proposed strategy were compared with previously employed strategies such as fuzzy logic control (FLC), state machine control strategy (SMCS), and equivalent fuel consumption minimization strategy (ECMS). The comparison was carried out in terms of the hydrogen fuel economy and the overall efficiency as the key factors. The resulting responses of the proposed MBA-based management strategy indicate that its performance is the best among the other strategies of SMCS, FLC, and ECMS in both the hydrogen fuel economy and overall efficiency