Optimizing boiler combustion parameters based on evolution teaching-learning-based optimization algorithm for reducing NO_x emission concentration

How to reduce a boiler's NOx emission concentration is an urgent problem for thermal power plants. Therefore, in this paper, we combine an evolution teaching-learning-based optimization algorithm with extreme learning machine to optimize a boiler's combustion parameters for reducing NOx emission concentration. Evolution teaching-learning-based optimization algorithm (ETLBO) is a variant of conventional teaching-learning-based optimization algorithm, which uses a chaotic mapping function to initialize individuals' positions and employs the idea of genetic evolution into the learner phase. To verify the effectiveness of ETLBO, 20 IEEE congress on Evolutionary Computation benchmark test functions are applied to test its convergence speed and convergence accuracy. Experimental results reveal that ETLBO shows the best convergence accuracy on most functions compared to other state-of-the-art optimization algorithms. In addition, the ETLBO is used to reduce boilers' NOx emissions by optimizing combustion parameters, such as coal supply amount and the air valve. Result shows that ETLBO is well-suited to solve the boiler combustion optimization problem

SCSO: A novel sine-cosine based swarm optimization algorithm for numerical function optimization

Many swarm optimization algorithms have been presented in the literature and these algorithms are generally nature-inspired algorithms. In this paper a novel sine-cosine based particle swarm optimization (SCSO) is presented. In SCSO, firstly particles are generated randomly in the search space. Personal best value and velocity of the particles are calculated and by using step. Calculated velocity is used for updating particles. The proposed algorithm is basic algorithm and approximately 30 rows MATLAB codes are used to implement the proposed algorithm. This short code surprisingly has high optimization capability. In order to evaluate performance and prove success of this algorithm, 14 well known numerical functions was used and the results illustrate that the proposed algorithm is successful in numerical functions optimization

An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems

Recently, interest in solving real-world problems that change over the time, so called dynamic optimisation problems (DOPs), has grown due to their practical applications. A DOP requires an optimisation algorithm that can dynamically adapt to changes and several methodologies have been integrated with population-based algorithms to address these problems. Multi-population algorithms have been widely used, but it is hard to determine the number of populations to be used for a given problem. This paper proposes an adaptive multi-population artificial bee colony (ABC) algorithm for DOPs. ABC is a simple, yet efficient, nature inspired algorithm for addressing numerical optimisation, which has been successfully used for tackling other optimisation problems. The proposed ABC algorithm has the following features. Firstly it uses multi-populations to cope with dynamic changes, and a clearing scheme to maintain the diversity and enhance the exploration process. Secondly, the number of sub-populations changes over time, to adapt to changes in the search space. The moving peaks benchmark DOP is used to verify the performance of the proposed ABC. Experimental results show that the proposed ABC is superior to the ABC on all tested instances. Compared to state of the art methodologies, our proposed ABC algorithm produces very good results

Multi-objective optimisation using the Bees Algorithm

In the real world, there are many problems requiring the best solution to satisfy numerous objectives and therefore a need for suitable Multi-Objective Optimisation methods. Various Multi-Objective solvers have been developed recently. The classical method is easily implemented but requires repetitive program runs and does not generate a true "Pareto" optimal set. Intelligent methods are increasingly employed, especially population-based optimisation methods to generate the Pareto front in a single run. The Bees Algorithm is a newly developed population-based optimisation algorithm which has been verified in many fields. However, it is limited to solving single optimisation problems. To apply the Bees Algorithm to a Multi- Objective Optimisation Problem, either the problem is converted to single objective optimisation or the Bees Algorithm modified to function as a Multi- Objective solver. To make a problem into a single objective one, the weighted sum method is employed. However, due to failings of this classical method, a new approach is developed to generate a true Pareto front by a single run. This work also introduces an enhanced Bees Algorithm. A new dynamic selection procedure improves the Bees Algorithm by reducing the number of parameters and new neighbourhood search methods are adopted to optimise the Pareto front. The enhanced algorithm has been tested on Multi-Objective benchmark functions and the classical Environmental/Economic power Dispatch Problem (EEDP). The results obtained compare well with those produced by other population- based algorithms. Due to recent trends in renewable energy systems, it is necessary to have a new model of the EEDP. Therefore, the EEDP was amended in conjunction with the Bees Algorithm to identify the best design in terms of energy performance and carbon emission reduction by adopting zero and low carbon technologies. This computer-based tool supports the decision making process in the design of a Low-Carbon City.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Solution of combined heat and power economic dispatch problem using genetic algorithm.

Masters Degree. University of KwaZulu-Natal, Durban.The combination of heat and power constitutes a system that provides electricity and thermal energy concurrently. Its high efficiency and significant emission reduction makes it an outstanding prospect in the future of energy production and transmission. The broad application of combined heat and power units requires the joint dispatch of power and heating systems, in which the modelling of combined heat and power units plays a vital role. This research paper employed genetic algorithm, artificial bee colony, differential evolution, particle swarm optimization and direct solution algorithms to evaluate the cost function as well as output decision variables of heat and power in a system that has simple cycle cogeneration unit with quadratic cost function. The system was first modeled to determine the various parameters of combined heat and power units in order to solve the economic dispatch problem with direct solution algorithm. In order for modelling to be done, a general structure of combined heat and power must be defined. The system considered in this research consists of four test units, i.e. two conventional power units, one combined heat and power unit and one heat-only unit. These algorithms were applied to on the research data set to determine the required decision variables while taking into account the power and heat units, operation bound of power and heat-only units as well as feasible operation region of the cogeneration unit. Power and heat output decision variables plus cost functions from Genetic Algorithm, differential evolution, Particle Swarm Optimization and artificial bee colony were determined using codes. Also, the decision variables and cost function value were obtained by calculations using direct solution algorithm. The findings of the research paper show that there are different ways in which combined heat and power economic dispatch variables can be determined, which include genetic algorithm, differential evolution, artificial bee colony, particle swarm optimization and direct solution algorithms. However, each solution method allows for different combined heat and power output decision variables to be found, with some of the methods (particle swarm optimization and artificial bee colony) having setbacks such as: large objective function values, slower convergence and large number solution. The analysis revealed that the differential evolution algorithm is a viable alternative to solving combined heat and power problems. This is due in most part to its faster convergence, minimum cost function value, and high quality solution which are diverse and widespread, more as a result of its effective search capability than genetic algorithm, particle swarm optimization, direct solution and artificial bee colony algorithms. The methods investigated in this research paper can be used and expanded on to create useful and accurate technique of solving combined heat and power problems

Multi-objective optimisation using the Bees Algorithm

In the real world, there are many problems requiring the best solution to satisfy numerous objectives and therefore a need for suitable Multi-Objective Optimisation methods. Various Multi-Objective solvers have been developed recently. The classical method is easily implemented but requires repetitive program runs and does not generate a true "Pareto" optimal set. Intelligent methods are increasingly employed, especially population-based optimisation methods to generate the Pareto front in a single run. The Bees Algorithm is a newly developed population-based optimisation algorithm which has been verified in many fields. However, it is limited to solving single optimisation problems. To apply the Bees Algorithm to a Multi- Objective Optimisation Problem, either the problem is converted to single objective optimisation or the Bees Algorithm modified to function as a Multi- Objective solver. To make a problem into a single objective one, the weighted sum method is employed. However, due to failings of this classical method, a new approach is developed to generate a true Pareto front by a single run. This work also introduces an enhanced Bees Algorithm. A new dynamic selection procedure improves the Bees Algorithm by reducing the number of parameters and new neighbourhood search methods are adopted to optimise the Pareto front. The enhanced algorithm has been tested on Multi-Objective benchmark functions and the classical Environmental/Economic power Dispatch Problem (EEDP). The results obtained compare well with those produced by other population- based algorithms. Due to recent trends in renewable energy systems, it is necessary to have a new model of the EEDP. Therefore, the EEDP was amended in conjunction with the Bees Algorithm to identify the best design in terms of energy performance and carbon emission reduction by adopting zero and low carbon technologies. This computer-based tool supports the decision making process in the design of a Low-Carbon City

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors