An integrated hydrochemical, statistical, and geospatial approach was adopted in the present study to evaluate the groundwater quality of the Mangura Nala watershed in the state of Bihar, India. A total of 154 groundwater samples were collected during two seasons (77 samples from each season) and analysed for various physico-chemical parameters, major ions, and fluoride concentrations. The groundwater of the area was characterised by Ca-Mg-HCO3 and Ca-Mg-Cl water types during both seasons. The estimation of saturation states of the aqueous minerals and chloro-alkaline indices suggested dominant impacts of sediment–water interaction and ion exchange processes in the groundwater. The ANOVA test depicted seasonal changes in groundwater quality, while the multivariate analyses, including principal component analysis and cluster analysis, clearly recognised potential water quality parameters deteriorating the groundwater quality in the study area. Around 30–45
This study presents an integrated artificial neural network (NN)—based predictive modelling and meta-heuristic optimisation framework for multi-response optimisation of wire electro-discharge machining (Wire EDM) parameters during machining of Hastelloy C-276. Due to its high strength and low thermal conductivity, Hastelloy C-276 is difficult to machine using conventional techniques, necessitating advanced optimisation strategies. Six process parameters—pulse-on time, pulse-off time, arc-on time, arc-off time, wire feed, and servo voltage—were investigated using a Taguchi L27 orthogonal array. Four key performance responses were evaluated: material removal rate (MR), kerf width (WK), surface roughness (S), and recast layer thickness (RLT). An NN model was developed to predict machining responses, and its weights were optimised using four meta-heuristic algorithms: Genetic Algorithm (G-A), Particle Swarm Optimisation (P-S-O), Grey Wolf Optimisation (G-W-O), and Whale Optimisation Algorithm (W-O-A). Model performance was assessed using R2, RMSE, and MAPE metrics. Among the hybrid models, NN-G-W-O demonstrated superior predictive accuracy and convergence stability. Comparative analysis with static multi-objective decision-making techniques (M-O-O-R-A and AHP) indicated improved robustness of the hybrid optimisation framework within the investigated parameter space. Sensitivity analysis and Pareto optimisation further clarified parameter influence and trade-offs among responses. The proposed framework provides a reliable and systematic approach for multi-response optimisation in advanced machining applications.
The performance and efficiency of closed-loop hydraulic power drives (CHPD) are highly influenced by varying load conditions, making their optimization essential for industrial applications such as mining, manufacturing, robotics, and automotive systems. A key application of CHPD is in chairlift man-riding systems (CMRS), where precise control and energy efficiency are critical. This study focuses on the performance optimization of closed-loop hydraulic drives, specifically examining load-dependent dynamics under two distinct loading conditions: the unilateral loading condition (ULC) and the bilateral loading condition (BLC). Such loading patterns are widely applied to CMRS, which utilizes closed-loop hydraulic drive systems. The research evaluates system behavior across varying operational speeds, analyzing key performance metrics, including total power consumption, efficiency, and the load cycle of the drive unit. A novel real-time optimization framework is proposed to dynamically adjust system parameters, thereby enhancing responsiveness and minimizing energy losses under varying load conditions. Simulations conducted in MATLAB/Simulink® were validated through experimental testing, ensuring accuracy and reliability. Findings indicate that power consumption is higher under BLC than ULC, with increases of 0.8
An eco-friendly and cost-effective ligand-free protocol has been developed for the direct hydroxylation of aryl halides in aqueous medium using easily accessible Al2O3-supported Ni nanoparticles as a stable heterogeneous catalyst in the presence of a strong base KOH (potassium hydroxide), where the aliphatic halides remained unaffected. A wide variety of aryl halides, such as iodides, bromides, and chlorides, were successfully hydroxylated and converted into their corresponding phenols with yields ranging from 80 % to 91 %. The crystalline nature of the synthesized nanocatalyst has been analysed by powder X-ray diffraction, which exhibited that the synthesized nanocatalyst consists of metallic Ni supported by Al2O3. The high-resolution XPS spectra of the Ni 2p region demonstrate the predominant presence of metallic nickel (Ni0) on the surface of the synthesized nanocatalysts. The TEM and SEM images distinctly showcased the crystalline nature of the synthesized nanocatalyst, revealing a well-defined architecture. Moreover, the catalyst was recyclable for up to 5 catalytic runs without reducing their catalytic performance. We have also performed the density functional theory to gain insights into the role of the Ni@Al2O3 hybrid catalyst in the hydroxylation reaction of haloarenes.
Battery thermal management system (BTMS) with conventional natural/forced air-cooling encounter following challenges: high temperature in the battery, non-uniform temperature across battery, elevated temperature at cell-terminals, and difficulty of heat removal from central region of BP. To overcome these limitations a novel air-cooled BTMS is developed, where strips, plates, and tubes of aluminium, and a secondary air-inlet at the bottom of the BP are included in a step-by-step manner to address these problems. Three-dimensional CFD modelling are performed with six BTMS models considering 2C and 4C rates of discharge. It is found that including these elements in the novel BTMS significantly improves the conductive heat removal, while installation of tubes reduce the flow resistance thereby increasing local air velocity and effectively cooling the central region of the BP. Best performance is obtained with the BTMS model comprising of the strips, plates, tubes, and the additional air-inlet. The novel proposed model achieved a substantial 22 % reduction in peak temperature and 39 % reduction in Delta T compared to conventional air-cooling systems. Furthermore, the proposed model outperforms widely adopted U-type, Z-type, T-type, and fin-type forced air-cooling designs, demonstrating superior thermal uniformity, simplicity, cost-effectiveness, and strong potential for real-world application.
