The hydrocarbons components are formed in multiple phase flow of gasses in a variation of temperature, pressure and composition, which results in flow change for hydrocarbon fluid. The presence of solids can cause severe problems in oil and gas production and transmission systems. Among them, the frequently occurring solid precipitation problems are due to hydrate formation which is thoroughly studied in this research. Hydrates provide a significant risk for all high-pressure natural gas transmissions, including connecting lines and manifold systems in oceanic transportation of compressed natural gas. Better safety and reliability conditions are very important in high-pressure natural gas transportation systems to properly analyze hydrate issues in the pipeline. The pressure and temperature variations of a system are one of the major causes which may lead to hydrate formation in hydrocarbon transmission. Fluid flow through different channels, equipment, production tubing and transmission pipeline were producing unstable temperature and pressure in hydrocarbon production and exploration system may cause the formation of a hydrate.

In the energy management of district cooling plants, the thermal energy storage tank is critical. As a result, it is essential to keep track of TES results. The performance of the TES has been measured using a variety of methodologies, both numerical and analytical. In this study, the performance of the TES tank in terms of thermocline thickness is predicted using an artificial neural network, support vector machine, and k-nearest neighbor, which has remained unexplored. One year of data was collected from a district cooling plant. Fourteen sensors were used to measure the temperature at different points. With engineering judgement, 263 rows of data were selected and used to develop the prediction models. A total of 70% of the data were used for training, whereas 30% were used for testing. K-fold cross-validation were used. Sensor temperature data was used as the model input, whereas thermocline thickness was used as the model output. The data were normalized, and in addition to this, moving average filter and median filter data smoothing techniques were applied while developing KNN and SVM prediction models to carry out a comparison. The hyperparameters for the three machine learning models were chosen at optimal condition, and the trial-and-error method was used to select the best hyperparameter value: based on this, the optimum architecture of ANN was 14-10-1, which gives the maximum R-Squared value, i.e., 0.9, and minimum mean square error. Finally, the prediction accuracy of three different techniques and results were compared, and the accuracy of ANN is 0.92%, SVM is 89%, and KNN is 96.3%, concluding that KNN has better performance than others.

As the world becomes more urbanized, the market for petroleum products increases. The supply of crude oil-based products such as diesel, gasoline, and natural gas is limited. Furthermore, natural resources are finite and their reservoirs are located in certain parts of the globe. Countries with low to no fossil fuel resources are experiencing a scarcity of petroleum products, necessitating the exploration of alternative energy resources. In this research, tests regarding the exhaust particulate emission, sound pressure level, and performance have been carried out using samples from diesel and biodiesel (waste cooking oil) blended fuel. Two fuel samples have been used, B25 (biodiesel 25% and 75% diesel) and 100% diesel as a baseline in a CI engine at constant RPM of 1350 and variable loads of 0.0 to 1.6 at an interval of 0.2Kg-m. The results show that particulate emissions are reduced by about 7.29% when using biodiesel blended fuel, whereas brake-specific fuel consumption of biodiesel blended fuel has decreased as brake power increased, and brake thermal efficiency increased as brake power increased. The sound pressure level was measured from different locations of the engine (back, front, left) and for varying load. The results show that B25 produces less noise than D100 in each case.

This paper addresses a design optimization of a gas turbine (GT) for marine applications. A gain-scheduling method incorporating a meta-heuristic optimization is proposed to optimize a thermodynamics-based model of a small GT engine. A comprehensive control system consisting of a proportional integral (PI) controller with additional proportional gains, gain scheduling, and a min-max controller is developed. The modeling of gains as a function of plant variables is presented. Meta-heuristic optimizations, namely a genetic algorithm (GA) and a whale optimization algorithm (WOA), are applied to optimize the designed control system. The results show that the WOA has better performance than that of the GA, where the WOA exhibits the minimum fitness value. Compared to the unoptimized gain, the time to reach the target of the power lever angle is significantly reduced. Optimal gain scheduling shows a stable response compared with a fixed gain, which can have oscillation effects as a controller responds. An effect of using bioethanol as a fuel has been observed. It shows that for the same input parameters of the GT dynamics model, the fuel flow increases significantly, as compared with diesel fuel, because of its low bioethanol heating value. Thus, a significant increase occurs only at the gain that depends on the fuel flow.

Thermal energy storage plays an important role in the energy management and has got great attention for many decades; stratification is a key parameter to be responsible for the performance of the stratified thermal energy storage tank. In this paper detailed study of modelling techniques used to analyse thermal energy storage has been conducted. The division of literature has been made as numerical, analytical, and artificial neural network-based. Numerical modelling being very physical based and required more specific software’s tools remain costly and computationally very complex at the same time it provides the detailed insights into the system, analytical model provide the exact solutions but need some assumptions which make the system unrealistic in some cases but is easy and flexible in terms of computational requirements, ANN though recently used modelling technique is a black box model which merely needs the data rather than any physical based complex calculations is attracting the scientific community.

It is very crucial to avoid hydrate formation in deep water natural gas pipeline and it has posed challenges to flow assurance. Although the ideal methods to find the hydrate formation conditions by temperature, pressure and composition of flowing gas mixtures by experimentally. But performing such methods maybe impractical as it involves the assumption of infinite number of hydrate formation prediction conditions. A novel method of artificial intelligence (AI) modelling for methane gas hydrate for subsea gas pipelines has been developed. It will help to find hydrate formation conditions for subsea pipeline. The correlations are based on the temperature with and without concentration of inhibitors during gas hydrate formation. The correlations between temperature and pressure are measured using polynomial and Fourier equations by incorporating AI optimization techniques of genetic algorithm (GA). All correlations are computed in the range of temperature 250 to 290 K and pressure 1.6 to 4.12 Mpa. The performance of developed algorithm was evaluated by comparing existing experimental data with measured correlations.