A review on the Application of Empirical Models to Hydrate Formation Prediction

In deepwater hydrocarbon transportation pipeline, the production may decrease and operational cost and time are increasing due to the growth rate of hydrate. The pressure of deepwater pipeline is comparatively high, so it is entirely possible to form the hydrate formation conditions and pose a major operational and safety challenge. This work provides a review on empirical models for hydrate formation prediction in deepwater gas pipeline. The correlation and empirical models are presented with the hydrate formation temperature described as a function of pressure and gas gravity. This study could possibly provide a milestone for predicting PVT and heat transfer effects on hydrate formation in deepwater pipeline

Analysis of Coverage and Area Spectral Efficiency under Various Design Parameters of Heterogeneous Cellular Network

As day by day the population is increasing, the use of mobile phones and different applications is increasing which requires high data rate for transmission. Homogeneous cellular network cannot fulfill the demand of mobile users, so creating a heterogeneous cellular network (HCN) is a better choice for higher coverage and capacity to fulfil the increasing demand of upcoming 5G and ultra-dense cellular networks. In this research, the impact of antenna heights and gains under varying pico to macro base stations density ratio from 2G to 5G and beyond on two-tier heterogeneous cellular network has been analyzed for obtaining optimum results of coverage and area spectral efficiency. Furthermore, how the association of UEs affects the coverage and ASE while changing the BSs antenna heights and gains has been explored for the two-tier HCN network model. The simulation results show that by considering the maximum macro BS antenna height, pico BS antenna height equal to user equipment (UE) antenna height and unity gains for both macro and pico tiers, the optimum coverage and area spectral efficiency (ASE) for a two-tier fully loaded heterogeneous cellular network can be obtained

Modelling Techniques Used in The Analysis of Stratified Thermal Energy Storage: A Review

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

Design Optimization of a Gas Turbine Engine for Marine Applications: Off-Design Performance and Control System Considerations

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

Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data

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

Cyclic Path Planning Of Hyper-Redundant Manipulator Using Whale Optimization Algorithm

This paper develops a path planning algorithm of hyper-redundant manipulators to achieve a cyclic property. The basic idea is based on a geometrical analysis of a 3-link planar series manipulator in which there is an orientation angle boundary of a prescribed path. To achieve the repetitive behavior, for hyper-redundant manipulators consisting of 3-link components, an additional path is chosen in such away so that it is a repetitive curve which has the same curve frequency with the prescribed end- effector path. To solve the redundancy resolution, meta-heuristic optimizations, namely Genetic Algorithm (GA) and Whale Optimization Algorithm (WOA), are applied to search optimal trajectories inside local orientation angle boundaries. Results show that using constant of the local orientation angle trajectories for the 3-link component, the cyclic properties can be achieved. The performance of the WOA shows very promising result where generally it obtains the lowest fitness value as compare with the GA. Depending on the complexity of the path planning, dividing the path into several stages via intermediate points may be necessary to achieve the good posture. The performance of the swarm based meta-heuristic optimization, namely the WOA, shows very promising result where generally it obtains the lowest fitness value as compare with the GA. Using the developed approach, not only the cyclic property is obtained but also the optimal movement of the hyperredundant manipulator is achieve