Duelist Algorithm Research Articles

Enhancing Techno Economic Efficiency of FTC Distillation Using Cloud-Based Stochastic Algorithm

A liquefied petroleum gas plant facility (LPGPF) is a series of binary distillation columns used to separate natural gas into four alkanes: ethane, propane, butane, and pentane. The conventional distillation column design consists of three binary distillation columns and six heat exchangers to perform the process. Each heat exchanger consumes immense energy to heat up the reboiler and condense the distillate. There are several process technologies that can minimize distillation column energy consumption. In this research, a fully thermally coupled distillation column (FTCDC) was proposed to minimize energy consumption by reducing the number of heat exchangers and tray columns. An FTCDC has the capability to reduce capital expenditure, operational expenditure, and total annual cost (TAC). The complexity of the FTCDC arises from its process integration. In each column, the intersection composition depends on complex mass and energy balances at the column inlet and outlet and each tray. Process integration, including material recycling and heat recovery, increases the complexity significantly. Moreover, the decision variables are multi-intersection composition for each column to achieve optimum objective function, increasing the number and complexity of the computational load such that effective stochastic optimization algorithms are required. The proposed method was designed using a rigorous vapor liquid equilibrium (VLE) FTCDC model and incorporated with recent stochastic optimization algorithms, such as a genetic algorithm, particle swarm optimization (PSO), an imperialist competitive algorithm, and a duelist algorithm, to determine hydrocarbon composition in the FTCDC intersection. To increase the efficiency and effectiveness of the FTCDC optimization design, cloud computing was utilized. The result was compared with conventional methods such as Fenske-Underwood-Gilliland, a Fenske-Underwood-Gilliland modification, and VLE. The optimization objective function is to minimize TAC with hydrocarbon composition in the FTCDC intersection as decision variables. The optimization using the VLE-PSO method reduces TAC up to 26.28%. All designs were validated using a rigorous model with Aspen HYSYS commercial software. This study's primary goal is to improve the performance of FTCDCs using stochastic algorithms and cloud-based computing capacity. The large amount of computation is handled by cloud-based computing resources, enabling reliability and durability.

A novel quality prediction method based on feature selection considering high dimensional product quality data

<p style='text-indent:20px;'>Product quality is the lifeline of enterprise survival and development. With the rapid development of information technology, the semiconductor manufacturing process produces multitude of quality features. Due to the increasing quality features, the requirement on the training time and classification accuracy of quality prediction methods becomes increasingly higher. Aiming at realizing the quality prediction for semiconductor manufacturing process, this paper proposes a modified support vector machine (SVM) model based on feature selection, considering the high dimensional and nonlinear characteristics of data. The model first improves the Radial Basis Function (RBF) in SVM, and then combines the Duelist algorithm (DA) and variable neighborhood search algorithm (VNS) for feature selection and parameters optimization. Compared with some other SVM models that are based on DA, genetic algorithm (GA), and Information Gain algorithm (IG), the experiment results show that our DA-VNS-SVM can obtain higher classification accuracy rate with a smaller feature subset. In addition, we compare the DA-VNS-SVM with some common machine learning algorithms such as logistic regression, naive Bayes, decision tree, random forest, and artificial neural network. The results indicate that our model outperform these machine learning algorithms for the quality prediction of semiconductor.</p>

Optimization of Dimensional, Surface Quality and Material Removal Rate in Turning using Response Surface Methodology and Duelist Algorithm

Optimization of Dimensional, Surface Quality and Material Removal Rate in Turning using Response Surface Methodology and Duelist Algorithm

Optimization of operating conditions of CO2-enhanced gas recovery and carbon sequestration

Currently, the climate changes are the big issue due to increasing in amount of CO2 foot print over the time. One of the most effective solutions to reduce CO2 emissions is to inject CO2 into the earth as known as carbon sequestration. CO2 injection has two advantages for CO2 gas storage and increases gas production in depleted reservoir or known as enhanced gas recovery (EGR). Although many studies of EGR model and characterization have been done and the results show that the application of EGR has potentially increased gas production and CO2 storage; however, EGR has not been applied in the field. The obstacle remaining in application of EGR is the significant cost related to EGR technology starting from CO2 procurement cost, transportation and operational cost. The operational costs of CO2 injection depend on the operating conditions of CO2 injection which is mass flow rate, pressure and temperature of CO2 injection. In this research, CO2 EGR and carbon sequestration processes were modeled by dividing into three parts, i.e., injection well, reservoir and production well. Pressure gradient in injection and production well was modeled using Beggs–Brill, while in reservoir by using Darcy equation. Temperature gradient for each part was modeled using mass and energy balances equations. The fluid properties were predicted using Peng–Robinson vapor–liquid equilibrium under commercial software HYSYS. Validation of injection and production well models was compared with PIPESIM, and the average mean deviations are 1.919% and 1.578%, respectively. Meanwhile, the validation of pressure and temperature gradient model compared to COMSOL Multiphysics software simulation in reservoir shows the average mean deviation of 0.2003% and 0.0002%, respectively. Based on the sensitivity analysis of the model, the profit will increase proportionally if mass flow rate and temperature increase; otherwise, it will decrease if CO2 injection pressure increases. Before optimization, the presence of CO2 injection in depleted gas reservoir with normal operating conditions can produce gas recovery of about 90.09% in which the profit generated is 6175.6 USD/day. EGR optimization has been performed using several recent stochastic algorithms, and the best optimization result was obtained by using Killer Whale Algorithm, duelist algorithm and Rain Water Algorithm. The optimization results show an increase in profit from 4453.8 USD/day to 12,331.9 USD/day or about 276.9% higher than the initial condition of the injection or without optimization. By using injection parameters that have been optimized, the CO2 that can be stored is 1486.01 tons.

Optimization of PID controller tuning parameters for multivariable system using Duelist algorithm

In this paper, one of the most current new stochastic optimization method is proposed to tune decentralized proportional-integral-derivative (PID) controller for a multi-input-multi-output (MIMO) system. PID controller is still the most used controller in industrial application due to its easy application in control system. However, it still having problem to be tuned correctly especially for MIMO system. The decentralized controllers in this paper will be tuned simultaneously using duelist algorithm (DA) method and optimal value of controller parameters is obtained from a minimum of integral absolute error (IAE) value. The optimization of the objective function is based on the evaluation of IAE when a step change in u1 and u2 is introduced to the system. Form the simulation result, the proposed method is observed to have a better performance than other popular tuning method.

Duelist algorithm for optimisation of safety instrumented system at distillation column based on RAMS + C

This paper describes the optimisation of safety instrumented system at distillation column based on reliability, availability, maintenance, safety, plus cost using duelist algorithm. Safety instrumented system maintain operating condition in the safe regime when normal operation over the allowable limits. Diversity redundancy was implemented as allocation redundancy. The requirement of safety instrumented system has been appointed by international standard, International Electrotechnical Commission 61508. The objective of this study is to achieve the lowest value of lifecycle cost that affected by average probability of failure on demand and spurious trip rate value. Average probability of failure on demand is involved by failure rate danger that has an effect on safety instrumented level value; meanwhile, spurious trip rate is influenced by failure rate safe that has an effect to the product losses caused unscheduled shutdown. The field instruments subsystems are provided by utilising one of or all different technologies. The best optimisation solution utilises vote 1 out of 1 for all subsystem with smart transmitter technology and air-operated actuator. Its solution obtained lifecycle cost 1,139,898 USD, average probability of failure on demand 7181 × 10−02/h and spurious trip rate 4844 × 10−05/h.

Operating Conditions Optimization of Steam Injection in Enhanced Oil Recovery Using Duelist Algorithm

Operating Conditions Optimization of Steam Injection in Enhanced Oil Recovery Using Duelist Algorithm

Techno Economic Optimization of Petlyuk Distillation Column Design Using Duelist Algorithm

Distillation process consume huge energy hence one of the method for reducing it is by applying Petlyuk configuration. In design stage, the important thing is the minimum number of trays. In this paper, four methods of Petlyuk configuration design were compared i.e. the short-cut method, the modified method using Winn equation, Vapor-Liquid Equilibrium (VLE) approach and the proposed method that incoorporated Duelist Algorithm Optimization (DAO) method and VLE approach. Optimization was performed by techno economic consideration. The objective is minimizing the total annual cost for operating period over 10 years. The maximum saving about 34,38% was provide when the actual number of trays at prefractionator and at main column are 3 and 109, respectively, and it's related operating condition.