Optimization

The quality of Tungsten Inert Gas (TIG) welding depends significantly on the appropriate selection of process parameters such as welding current, voltage, and welding speed. Improper parameter combinations often lead to defects in weld bead geometry, reduced penetration, and an enlarged heat-affected zone. To achieve a stable arc and improved weld integrity, it becomes necessary to determine the optimal combination of these parameters. Hence, the aim of this work is to develop and apply an Ant Colony Optimization (ACO) algorithm to minimize the arc length of TIG welds using MATLAB 2024. The research methodology involved formulating an objective function that relates the arc length to the key process variables—current (A), voltage (V), and welding speed (F). The ACO metaheuristic technique was employed to simulate the welding process, with each ant representing a possible parameter combination. The pheromone updating and heuristic information were used to guide the search towards optimal solutions, and the developed algorithm was implemented and coded in MATLAB 2024. The results of the optimization revealed that the ACO algorithm successfully determined the optimal process parameters of 100 A current, 19.27 V voltage, and 0.145 m/min welding speed, producing a minimum arc length of approximately 0 mm after correction. This indicated a highly concentrated and stable arc. The predicted results were compared with the literature model and found to be in good agreement, confirming the accuracy and robustness of the developed algorithm. The findings demonstrate that ACO is an effective metaheuristic technique for intelligent optimization of TIG welding parameters, capable of enhancing weld quality through efficient parameter selection.

The growing need for sustainable petroleum production and greenhouse gas reduction has driven interest in carbon dioxide (CO₂)-based Enhanced Oil Recovery (EOR) as a dual-purpose technology for increasing oil recovery while enabling carbon sequestration. This study investigates the potential of CO₂ injection for enhanced oil recovery and geo-storage in tight sandstone reservoirs within the Niger Delta Basin, Nigeria. Laboratory-based simulations and analytical modeling were used to evaluate the influence of injection rate, permeability, wettability, and fracture dip angle on oil displacement efficiency and CO₂ storage performance. Results show that at lower injection rates (0.2 mL/min), delayed breakthrough (0.85 PV) was observed with a recovery factor of 88.2%, while moderate rates (0.5 mL/min) achieved optimal recovery (91.4%) and balanced CO₂ retention (58.1%). Higher rates (1.0 mL/min) led to early breakthrough (0.55 PV) and reduced storage efficiency (54.9%). Overall, findings demonstrate that CO₂ injection enhances oil recovery by 85–93% and achieves up to 65% storage efficiency, depending on reservoir conditions. The study confirms that CO₂-EOR is technically feasible for the Niger Delta Basin, offering both economic and environmental benefits by improving hydrocarbon recovery and reducing carbon emissions.

This study focused on the optimization of organic fertilizer production from Chromolaena odorata (Siam weed) with the aim of determining the most suitable drying conditions for nutrient preservation and evaluating its effects on soil fertility and maize (Zea mays) growth performance. Fresh C. odorata leaves were collected, air-dried, and oven-dried under varying temperature (30–85 °C) and time (30–120 min) conditions. The effects of these parameters on nutrient composition (particularly nitrogen (N), phosphorus (P), and potassium (K)) were analyzed using Response Surface Methodology (RSM) based on a Central Composite Design (CCD). The optimized fertilizer product was applied to soil samples to assess changes in nutrient levels before and after maize cultivation. Results revealed that both drying temperature and time significantly influenced the nutrient composition of C. odorata. Nitrogen and phosphorus contents decreased with increasing temperature, whereas potassium concentration increased due to moisture reduction and mineral concentration effects. The optimal drying condition was found to be approximately 55 °C for 90 minutes, which preserved the highest levels of essential nutrients. Post-sowing soil analysis indicated improved macronutrient and micronutrient concentrations following the application of the optimized organic fertilizer. Maize plants grown in treated soil exhibited superior vegetative growth compared to the control, with notable increases in plant height, stem diameter, and dry biomass yield. The findings demonstrate that Chromolaena odorata can serve as an effective and sustainable source of organic fertilizer. Its utilization not only enhances soil fertility and crop productivity but also provides an environmentally friendly means of managing this invasive weed species.

As the world continues to embrace cleaner and smarter energy solutions, there's a growing need for tools that not only design microgrids but also make them smarter, more responsive, and easier to manage. This project introduces an Intelligent Microgrid Management and Optimization System — a desktop application built with Python — designed to help users plan, optimize, and monitor solar-powered microgrid systems more efficiently. What sets this tool apart is its ability to pull live weather data (like sunlight levels and temperature) using the Open Weather Map API. With this, it can predict how much energy your solar panels might generate and how much power you’ll need, thanks to built-in machine learning models. The system then uses a genetic algorithm to figure out the best combination of solar panel size and battery capacity to meet your energy needs while keeping costs low. The application runs through a simple and responsive user interface (built with PyQt6), offering features like real-time graphs, a weather dashboard, and system control panels. It also supports SCADA-style monitoring, so users can see power generation, battery status, and energy demand in real time. Overall, this tool is designed to be both smart and user-friendly, making it useful not just for engineers and developers, but also for students, researchers, and organizations working on renewable energy solution

This project presents the optimization of solar inverter efficiency using machine learning algorithms to improve power generation accuracy and system reliability under varying environmental conditions. Traditional solar inverter systems and Maximum Power Point Tracking (MPPT) methods often experience limitations in adapting to fluctuations in solar irradiance, temperature, and shading conditions, leading to reduced efficiency and energy loss. To address these challenges, this study developed and evaluated machine learning models capable of predicting and optimizing inverter performance in real time. Environmental and operational data including irradiance, temperature, day, hour, and inverter performance metrics were collected from the NASA and NSRDB datasets for the University of Benin region. Data preprocessing techniques such as normalization, interpolation, and feature engineering were applied before model training. Three machine learning models — Random Forest (RF), Gradient Boosting Machine (GBM), and Artificial Neural Network (ANN) — were implemented and evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²). Results showed that the ANN model outperformed the other models with an MAE of 0.019, RMSE of 0.029, and R² value of 0.962. The optimized system achieved an efficiency improvement of 8.3% compared to conventional MPPT methods. The study further demonstrated the capability of machine learning algorithms to adapt to changing environmental conditions and improve solar inverter performance. The developed model was deployed using Django REST Framework for real-time prediction and monitoring. This research confirms that machine learning-based optimization can significantly enhance solar inverter efficiency, reduce energy losses, and contribute to sustainable and intelligent renewable energy systems.

This project investigates the effect of key drilling parameters on Rate of Penetration (ROP) using real-world field data from a selected well. The parameters analyzed include Weight on Bit (WOB), Rotational Speed (RPM), and mud properties such as Plastic Viscosity, Yield Point, and Gel Strength. The study aims to understand how variations in these parameters influence ROP and to identify combinations that could
enhance drilling efficiency. Microsoft Excel was used for organizing, calculating, and analyzing the data, with additional tools such as Solver applied for basic optimization. By focusing on a practical, data-driven approach, this work contributes to ongoing efforts in optimizing drilling operations, especially in regions where advanced software and models may be inaccessible. The findings provide insight into the practical relationships between operational parameters and ROP, and highlight opportunities for performance improvement in similar field environments

Given Nigeria's abundant agro-industrial wastes, the study focused on optimizing a ternary blend of cassava peels (CP), coconut husk (CH), and sawdust (SD) to maximize bioethanol yields. Unlike previous studies that examined these feedstocks individually, this work investigated their co-processing potential to overcome disposal challenges and enhance their utilization. The characterization of the feedstocks revealed diverse compositions: CP was rich in hemicellulose, CH presented a balanced composition, and SD was cellulose-rich but highly recalcitrant due to its high lignin content. Utilizing a {3,2} Simplex Lattice Design (SLD) across 15 experimental runs, a Special Quartic model was developed to elucidate the relationship between blend ratios and sugar yield. This model demonstrated high significance (F-value = 88.93, p < 0.0001) and an excellent fit (R² = 0.9916), highlighting substantial synergistic interactions, especially between CP and CH. The optimized blend, consisting of 66.7% CP, 16.7% CH, and 16.7% SD, yielded an impressive experimental sugar yield of 370.31 mg/g, which significantly surpassed the yields from individual feedstocks. Subsequent validation of this optimized blend involved acid pretreatment, enzymatic hydrolysis, and fermentation using Saccharomyces cerevisiae, resulting in an experimental ethanol yield of 0.0644 g ethanol/g biomass. This achievement represents 85.4% of the theoretical yield, confirming a high fermentation efficiency and validating the strategic blending as an effective waste-to-wealth strategy for sustainable bioenergy production

Optimization of process parameter to improve on weld joint quality has been at the centre of global research. Some optimization methods have produced welds of low strength and quality whereas , some have made remarkable improvements on the quality of welded joints. In this study, the SWARA-ARAS method was adopted to access its effect on the quality of the obtained welded joints. Stepwise Weight Assessment Ratio Analysis (SWARA) method was used to determine the geometric mean of weights for each of the output parameters that is the mechanical test and measurement results. Additive Ratio Assessment (ARAS) was applied to optimize these parameters by utilizing the weights generated by using SWARA. From applying the SWARA-ARAS method, weldment was found to possess the best input and output parameters

In this era, the best problem solving method is needed in all field irrespective of the complexity or simplicity of the problem. Researchers and developers are doing their best to make software’s and machines more potent and intelligent. This is the advantage of artificial intelligent in developing solutions to searching algorithms that are potent and optimal. The most potent highly developed investigate method in Artificial Intelligence is the genetic algorithm. Genetic algorithm was developed to get best result to a known difficulty premised on inheritance, collection, crossover, mutation and further method. It has been proven that genetic algorithm is the most potent, impartial optimization method for analyzing a solution with large space. this research have been able to define what is genetic algorithm, how it differs from other existing traditional search optimization method, review of ten (10) traditional techniques of finding the best route in a given network. Also the design of genetic algorithm, it’s implementation on finding the best route within 20 cities (point) which is invariably the travelling salesman problem (TSP), and areas of application of application of genetic algorithms. The best route is invariably the shortest path.