An evaluation of hydrothermal liquefaction (HTL) char is investigated in this work. Morphological studies, N₂ adsorption behavior, FTIR analysis, thermal behavior, and elemental composition are studied. The HTL char yield showed an increase with higher operating temperatures. It increased from 11.02% to 33% when the temperature increased from 573 K to 623 K. At lower temperatures, the residence time showed an impact on the yield, while close to the critical point, residence time became less impactful. Elemental analysis showed that both higher operating temperatures and longer residence times increased the nitrogen content of the chars from 0.32% to 0.51%. FTIR analysis suggested the char became more aromatic with the higher temperatures. The aliphatic groups present diminished drastically with the increasing temperature. Residence time did not show a significant impact as much as the temperature when considering the functional group elimination. An increase in operating temperatures and residence times produced thermally stable chars. HTL char produced at the lowest operating temperature and showed both the highest surface area and pore volume. When temperature and residence time increase, more polyaromatic char is produced due to carbonization.

Fatigue cracks are a major defect in metal alloys, and specifically, their study poses defect evaluation challenges in aluminum aircraft alloys. Existing inline inspection tools exhibit measurement uncertainties. The physical-based methods for crack growth prediction utilize stress analysis models and the crack growth model governed by Paris’ law. These models, when utilized for long-term crack growth prediction, yield sub-optimum solutions and pose several technical limitations to the prediction problems. The metaheuristic optimization algorithms in this study have been conducted in accordance with neural networks to accurately forecast the crack growth rates in aluminum alloys. Through experimental data, the performance of the hybrid metaheuristic optimization–neural networks has been tested. A dynamic Levy flight function has been incorporated with a chimp optimization algorithm to accurately train the deep neural network. The performance of the proposed predictive model has been tested using 7055 T7511 and 6013 T651 alloys against four competing techniques. Results show the proposed predictive model achieves lower correlation error, least relative error, mean absolute error, and root mean square error values while shortening the run time by 11.28%. It is evident through experimental study and statistical analysis that the crack length and growth rates are predicted with high fidelity and very high resolution.

The growing population and the rise in urbanization have made managing water a critical concern around the world in recent years. Globally, flooding is one of the most devastating natural disasters. Flood risk mitigation relies heavily on accurate and consistent streamflow forecasts. Pakistan Upper Indus Basin (UIB) is most vulnerable to flooding. Floods have become more frequent in recent decades. UIB can be divided into sub-regions due to its landscape variability, and its collective impact is most prominent in the Massam region. UIB hydrological and meteorological station observations have been used to study seasonal hydro-meteorological variations. To predict flooding, this study proposes a hybrid model combining artificial neural networks as multi-layer perceptron (MLPs) in feed-forward mode, along with empirical mode decomposition (EMD). Data collected by the surface-water hydrology project and Pakistan Meteorological Department from 1960 to 2012, 1969 to 2012, and 1972 to 2012 have been utilized from 17 locations. Statistical parameters and Nash–Sutcliffe Efficiency were measured to analyze the model’s prowess. As a result, decomposition-based models perform better than AI-based models when it comes to prediction accuracy. MLPQTP-EMD performed exceptionally better than competing AI models. The results are further validated by performing a peak value analysis during the flooding season (June–September) achieving a remarkable 91.3% score adding a 5.6% increase by EMD for input data achieving 39.3–32.3% statistical indices scores.

Most industrial processes generate raw heat. To enhance the efficiency of industrial operations, this raw heat is recovered. Thermoelectric generators (TEG), as solid state devices, provide an excellent application of heat recovery in the form of most manageable electrical power. This work presents a novel MPPT controller based on the Mud Ring Optimization algorithm for a centralized Thermoelectric Generator (TEG) under dynamic thermal gradients. The existing stochastic optimization algorithm for Maximum Power Point Tracking (MPPT) control in renewable energy systems exhibits several limitations that affect its performance in MPPT control. The convergence speed, local minima trap, hyper parameters’ sensitivity toward the population size, acceleration coefficients, and the stopping criterion all impact the convergence stability. In addition to these limitations, sensor noise sensitivity in measurement fluctuates the control system leading to reduced performance. Therefore, the careful design and implementation of the MRO algorithm is crucial to overcome these limitations and achieve a satisfactory performance in MPPT control. The results of this study contribute to developing more efficient MPPT control of TEG systems and implementing renewable energy technologies. The algorithm effectively tracks the maximum power point in dynamic thermal environments and increases the power output compared to conventional MPPT methods. The findings illustrate the efficacy of the proposed controller providing a higher power output (Avg. 99.95%) and faster response time (220 ms) under dynamic thermal conditions achieving 38–70% faster tracking of the GM in dynamic operating conditions.

The global energy demand, along with the proportionate share of renewable energy, is increasing rapidly. Renewables such as thermoelectric generators (TEG) systems have lower power ratings but a highly durable and cost-effective renewable energy technology that can deal with waste heat energy. The main issues associated with TEG systems are related to their vigorous operating conditions. The dynamic temperature gradient across TEG surfaces induces non-uniform temperature distribution (NUTD) that significantly impacts the available output electrical energy. The mismatching current impact may lower the energy yield by up to 70% of extractable thermal energy. As a solution, a hybrid general regression neural network (GRNN) orca predation algorithm (OPA) is proposed; backpropagation limitations are minimized by utilizing the stochastic optimization algorithm named OPA. The conclusions are evaluated and contrasted with highly improved versions of the conventional particle swarm optimization (PSO), grey wolf optimizer (GWO), and Harris hawk optimization (HHO). A detailed analytical and statistical analysis is carried out through five distinct case studies, including field stochastic data study, NUTD, varying temperature, and load studies. Along with statistical matrix errors such as MAE, RMSE, and RE, the results are assessed in terms of efficiency, tracking, and settling time. The results show that superior performance is achieved by the proposed GRNN-OPA based MPPT by 35% faster tracking, and up to 90–110% quicker settling time which, in turn, enables the 4–8% higher energy accumulation over a longer period of operation. A low-cost experimental setup is devised to further validate the practicality of the proposed techniques. From such comprehensive analysis, it can be safely concluded that the proposed GRNN-OPA successfully undertakes the drawbacks of existing classical MPPT methods with higher efficiency.

Supercapacitors are required to store energy from renewable resources to ensure a pollutant-free environment. To further encourage its study, researchers are interested in introducing green methods to produce electrode materials. Green synthesis is an innovative and emerging field because plant extracts are the best substitute for toxic chemicals. They are considered eco-friendly and cost-effective. In this work, two plant extracts, orange juice (ORJ) and lemon juice (LMJ), are used to synthesize the Sr_0.8Ce_0.2Fe_0.8Co_0.2O₃ perovskite using the auto-combustion method. The electrochemical performance of Sr_0.8Ce_0.2Fe_0.8Co_0.2O₃ made from LMJ and ORJ is compared to check their effectiveness. LMJ proved to be a better reducing agent than ORJ with a higher specific capacity of 300 C/g (544 F/g) at 1 A/g current density due to increased oxygen vacancies and surface area. These findings show that green-synthesized perovskites can be utilized in high-performance hybrid supercapacitor devices.