Black-Hole Optimization Applied to the Parametric Estimation in Distribution Transformers Considering Voltage and Current Measures

A non-linear model for farm optimization with adequate and limited water supplies: Application to the South-east Anatolian Project (GAP) Region

Time varying acceleration coefficients particle swarm optimisation (TVACPSO): A new optimisation algorithm for estimating parameters of PV cells and modules

Nonlinear disjunctive programming models for the synthesis of heat integrated distillation sequences

In this paper a novel model for analyzing the transient state of the distribution transformers is proposed. The presented model is as simple that the simulation process can be conducted so fast and easy and also its application in the form of a two port element in power network is possible. By considering the complexity of the analytical methods, Particles Swarm Optimization (PSO) algorithm is applied for estimation of the parameters of the transient model of the transformers. In order to do that, related tests were performed on a 2.5 MVA and 6300/420 V distribution transformers, after that, the desired parameters were estimated by the implemented PSO algorithm. Finally, by comparing the experimental and the estimated values, the reliability of the PSO algorithm in this case was evaluated. Also, a comparison between the obtained values in this research and the results of Genetic Algorithm (GA) and the analytical method were carried out. The result reveals the more capabilities and accuracies of the PSO algorithm.

Improved Adaptive PSO-based Gain Tuning for PID Controllers in Utility Boilers

A model to optimize rack layout in a retail store

Maritime search and rescue (SAR) plays a very important role in emergency waterway traffic situations, which is supposed to trigger severe personal casualties and property loss in maritime traffic accidents. The study aims to exploit an optimal allocation strategy with limited SAR resources deployed at navigation-constrained coastal islands. The study formulates the problem of SAR resource allocation in coastal areas into a non-linear optimization model. We explore the optimal solution for the SAR resource allocation problem under constraints of different ship and aircraft base station settings with the help of an enhanced particle swarm optimization (EPSO) model. Experimental results suggest that the proposed EPSO model can reasonably allocate the maritime rescue resources with a large coverage area and low time cost. The particle swarm optimization and genetic algorithm are further implemented for the purpose of model performance comparison. The research findings can help maritime traffic regulation departments to make more reasonable decisions for establishing SAR base stations.

A nonlinear programming (NLP) model has been established to describe the process of low rank coal pyrolysis, and the model was validated through comparison with pilot or semi-industry technologies. Results showed good agreement with actual production values, suggesting that the model can be regarded as a general one for simulating low rank coal pyrolysis. On the basis of this model, the products’ distributions and energy penalties can be obtained from the coal pyrolysis process. In this study, the NLP model was used to simulate typical five pyrolysis processes such as the Toscoal process, Lurgi–Ruhrgas process, Char Oil Energy Development technology, Dalian University of Technology process, and Multistage Rotary Furnace technology from China Coal Research Institute, and Hulunbuir lignite was taken as a coal sample. Combined with the simulation results from the NLP model, effects of the key parameters on the performance of the pyrolysis technologies are investigated and a comprehensive evaluation in terms ...

Interpretable linear and nonlinear quantitative structure-selectivity relationship (QSSR) modeling of a biomimetic catalytic system by particle swarm optimization based sparse regression

Parameter Estimation for One-Dimensional Chaotic Systems by Guaranteed Algorithm and Particle Swarm Optimization

The paper presents the results of the double-circuit overhead power line phase conductors arrangement optimization, in order to reduce the maximum values of electric field and magnetic flux density. The unique fitness function was obtained by normalizing the values of the electric field and magnetic flux density in relation to the corresponding prescribed limit values in the Republic of Serbia. The influence of sagged conductor and the distribution of the electric charges along the conductor were taken into account in the calculations. The considered nonlinear optimization problem is solved by applying two metaheuristic methods: Genetic Algorithm and Particle Swarm Optimization. The proposed optimization methods were applied to the 400 kV double-circuit overhead power line. The influence of different length of insulator is considered. In order to evaluate the efficiency of both proposed optimization methods, the results were compared with the results obtained on the typical 400 kV double-circuit overhead power line

A comparative analysis of particle swarm optimization and differential evolution algorithms for parameter estimation in nonlinear dynamic systems

Multiple attribute decision making based on novel score function of interval-valued intuitionistic fuzzy values, score matrix, and nonlinear programming model

In this paper, the optimal design of forced draft cooling tower is carried out by establishing a nonlinear programming model. Firstly, the thermodynamic property and Kinetic characteristics of forced draft cooling tower are analyzed. Then a nonlinear optimization model is developed based on the above analysis results. The changes of working condition caused by meteorological conditions and water demand in the main production process is explored when the cooling tower is designed. Finally, one case is employed to validate the effectiveness of the proposed optimization method. The results show that the annual operating cost is reduced by 21.6% and the annual comprehensive cost is reduced by 15.9% compared with the results of conventional method. The developed method in this study can effectively reduce the comprehensive cost by considering the changes of working condition when a cooling tower is designed.

Thermal recovery methods are viable and commonly used to recover heavy oil reservoirs by reducing oil viscosity and improving oil displacement. However, there are many challenges associated with conventional steam injection methods. These challenges include the significant heat energy losses before steam reaches the reservoir, the high cost of steam generation and injection, as well as the emission of greenhouse gases. Therefore, it is essential to introduce a heavy oil recovery approach in which steam can be generated downhole to overcome these challenges associated with conventional steam injection methods. However, this novel heavy-oil recovery method has several designs and operational parameters that must be efficiently optimized, to achieve maximum recovery from heavy-oil reservoirs with less cost and minimum environmental impact. The objective of this work is to introduce a novel heavy-oil recovery technique using in-situ steam generated by downhole thermochemical reactions and investigate the key design and operational parameters of this complex recovery process. Modified self-adaptive differential evolution (MSaDE) and particle swarm optimization (PSO) methods are used in this work as global optimizer to find the optimum design and operation parameters to achieve the maximum net present value (NPV) and highest oil recovery (RF) of a heavy-oil reservoir after ten years of development. Comparison of the two proposed optimization methods is introduced as well. The results show that downhole thermochemical reactions can be used to generate in-situ steam, to efficiently reduce the heavy-oil viscosity and improve oil mobility. It has also shown that utilizing MSaDE and PSO methods to optimize the key components of this novel recovery process, significantly enhanced the recovery performance, in terms of higher NPV and RF. This study provides the first known in-depth optimization and uncertainty analysis to outline the significance of each design and operation parameter of the proposed novel thermochemical recovery process. This work showed and verified the concept of using downhole thermochemical reactions as an environmental-friendly solution to recover oil from heavy-oil reservoirs and is considered as a step forward to eliminate the greenhouse gases emission related to thermal recovery methods.