Rock fracture toughness (RFT) is one of the most critical indicators in rock mechanics and is used to determine how fractures propagate in processes such as hydraulic fracturing, rock blasting, tunnel excavation, geot...
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Rock fracture toughness (RFT) is one of the most critical indicators in rock mechanics and is used to determine how fractures propagate in processes such as hydraulic fracturing, rock blasting, tunnel excavation, geothermal energy extraction, and CO2 sequestration. Determining this fracture toughness on the basis of various rock parameters is an ongoing research area. Previous studies have established simple regression relationships between fracture toughness and individual parameters, achieving varying degrees of success. However, these models are often based on specific rocks and locations, with input parameters selected through intuitive judgment rather than quantitative analysis, leading to a lack of broad acceptance. In this study, a kepler optimization algorithm (KOA)-optimized BP algorithm was utilized alongside four unoptimized machine learning algorithms and one empirical formula to predict fracture toughness from a set of common geomechanical parameters. Unlike many previous studies, the normalized mutual information (NMI) method was employed in this study to analyze the sensitivity of these parameters to RFT and assess their importance to the model. Quantitative analysis identified 8 parameters-R, B, S, alpha, T, UCS, Cc, and nu-out of the fifteen potential input parameters as having the highest correlation with fracture toughness, addressing the previous issue of parameter selection on the basis of intuition rather than statistical analysis. This study developed a KOA-BP neural network model as a powerful tool for predicting rock fracture toughness, integrating 3 essential modules. The data processing module normalizes and partitions the dataset to ensure consistency. The KOA optimization module employs the kepler optimization algorithm to iteratively optimize the weights and biases of the BP neural network, addressing challenges such as premature convergence and parameter sensitivity. Lastly, the BP neural network module performs forward propagation and
Incorporating renewable energy sources (RESs) introduces a notable amount of uncertainty in the optimal planning and operation of electrical power grids. Under these circumstances, this paper proposes the application ...
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Incorporating renewable energy sources (RESs) introduces a notable amount of uncertainty in the optimal planning and operation of electrical power grids. Under these circumstances, this paper proposes the application of a recently introduced metaheuristic optimization technique to solve the stochastic optimal power flow (OPF) problem involving wind and solar power sources. The self-adaptive bonobo optimizer (SaBO) is used to minimize three distinct objective functions: (i) Total generation cost (TGC) minimization, including both thermal and wind/solar generation costs, (ii) Power loss minimization, (iii) Combined generation cost and emissions effect minimization. The costs associated with the stochastic generation of wind and solar power included direct costs, reserves and penalty costs from the overestimation and underestimation of available wind and solar power, respectively. The performance of the proposed algorithm is evaluated on two power systems: the modified IEEE 30-bus and the Algerian DZA 114-bus test systems. To demonstrate the efficacy of the SaBO, the obtained results have been compared with those obtained from the kepler optimization algorithm (KOA) and other recently published optimizers under the same case studies and constraints. The comparative results clearly show the superiority of the SaBO algorithm over all other well-known optimizationalgorithms provided in the literature for solving the OPF problem. This is evidenced by minimizing total generation costs of 781.2363 $/h for the modified IEEE 30-bus and 16,706.1630 $/h for the Algerian DZA-114-bus system. Furthermore, the integration of RES led to a notable 2.33% and 11.67% reduction in total generation cost for the IEEE 30-bus and Algerian DZA 114-bus systems, respectively, compared to their initial configurations without RESs. The promising findings highlight the powerful of the optimizer to solve non-linear and complex optimization problems in power systems.
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