Modelling and Optimization Techniques for Gas Hydrate Formation in Pipeline: A Review (Published)
In order to increase production and satisfy customer demands, engineers working in the oil and gas sectors are continuously faced with challenges pertaining to monitoring and improving pipeline flow processes. Considering the decrease in production costs resulting from obstructions caused by various flow assurance problems. In order to establish the simulation framework that has been widely used to improve hydrate formation prediction over the past few years, this paper provides guidance on justifiable hydrate formation prediction methodologies. This report evaluates papers on hydrate formation prediction and modeling from previous years using a systematic literature review process. This paper discusses a classification system for hydrate modeling techniques and their subcategories, including kinetic techniques, thermodynamic techniques, machine learning techniques, and simulation techniques. According to the results, computational and numerical methods are used because of the sustainable formation forecast of the hydrate scheme, even if a rising pattern is seen in the usage of simulation frameworks in conjunction with artificial intelligence approaches. Due to their propensity to handle ambiguity and uncertain data sets more effectively than numerical techniques, simulation and integrated frameworks are in high demand. However, numerical methods do a good job of handling deterministic data sets. Thus, this review helps researchers identify current flaws arising from flow optimization modeling. Finally, current shortcomings in flow optimization modeling were taken into consideration, opening up new research directions.
Keywords: Hydrate, Literature Review, Modelling, Simulation, optimization
Neural Network Prediction of Surface Roughness with Bearing Clearance Effect (Published)
In manufacturing industry, the quality of manufactured machine components, is determined by how well they follow a defined product’s criteria for dimensional accuracy, tool wear, and surface finish quality. For this reason, manufacturers must be able to regulate machining processes to ensure improved performance and service life of engineering components. This research work presents a study on the optimization of machining parameters for mild steel using artificial neural networks (ANNs). The focus is on developing an effective and efficient machining technique for mild steel by leveraging the capabilities of ANNs to predict optimal machining parameters. To bridge the gap between laboratory figures, model-simulated values, and real-world application, experiments were conducted to obtain data used in the research analysis. Levenberg-Marquardt method were utilized to train the ANNs, with input factors like depth of cut, bearing clearance, cutting speed, and feed rate considered, while the surface roughness of the cut, normalized within 0 to 1 range. A statistical measure of the surface roughness predicted using indicated MAPE value of 0.002% while the correlation coefficient (R) was 0.99995. The results showed that ANNs are a viable machining parameter optimization method and can improve product quality, while providing significant economic and production benefits.
Keywords: Artificial neural networks (ANN), Modelling, machining processes, optimization, predictive modelling., surface roughness, turning parameters