New immune multiobjective optimization algorithm and its application in boiler combustion optimization

In order to meet the requirements of combustion optimization for saving energy and reducing pollutant emission simultaneously,an immune cell subsets based multiobjective optimization algorithm（ICSMOA）is proposed.In the ICSMOA,the subset division operator and the immunological tolerance operation are defined.Preference can be easily addressed by using the subset division operator,and the distribution of the solutions can be guaranteed by the immunological tolerance operation.Using the ICSMOA,a group of Pareto optimal solutions can be obtained.However,by the traditional weighting method（WM）,only one solution can be obtained and it cannot be judged as Pareto optimal or not.In contrast to the solutions obtained by the repeatedly performed WM,the simulation results show that most solutions obtained by the ICSMOA are better than the solutions obtained by the WM.In addition,the Pareto front obtained by the ICSMOA is not as uniform as most classical multiobjective optimization algorithms.More optimal solutions which meet the preference set by the decision-maker can be obtained and they are very useful for industrial application.

The Application of Thermomechanical Dynamics (TMD) to Thermoelectric Energy Generation by Employing a Low Temperature Stirling Engine

A thermoelectric generation Stirling engine (TEG-Stirling engine) is discussed by employing a low temperature Stirling engine and the dissipative equation of motion derived from the method of thermomechanical dynamics (TMD). The results and mechanism of axial flux electromagnetic induction (AF-EMI) are applied to a low temperature Stirling engine, resulting in a TEG-Stirling engine. The method of TMD produced thermodynamically consistent and time-dependent physical quantities for the first time, such as internal energy ℰ(t), thermodynamic work Wth(t), the total entropy (heat dissipation) Qd(t)and measure or temperature of a nonequilibrium state T˜(t). The TMD analysis produced a lightweight mechanical system of TEG-Stirling engine which derives electric power from waste heat of temperature (40˚CT100˚C) by a thermoelectric conversion method. An optimal low rotational speed about 30θ′(t)/(2π)60(rpm) is found, applicable to devices for sustainable, clean energy technologies. The stability of a thermal state and angular rotations of TEG-Stirling engine are specifically shown by employing properties of nonequilibrium temperature T˜(t), which is also applied to study optimal fuel-injection and combustion timings of heat engines.

Comparative study of low NO_(x) combustion optimization of a coal-fired utility boiler based on OBLPSO and GOBLPSO

To reduce NO_(x) emissions of coal-fired power plant boilers,this study introduced particle swarm optimization employing opposition-based learning(OBLPSO)and particle swarm optimization employing generalized opposition-based learning(GOBLPSO)to a low NO_(x) combustion optimization area.Thermal adjustment tests under different ground conditions,variable oxygen conditions,variable operation modes of coal pulverizer conditions,and variable first air pressure conditions were carried out on a 660 MW boiler to obtain samples of combustion optimization.The adaptability of PSO,differential evolution algorithm(DE),OBLPSO,and GOBLPSO was compared and analyzed.Results of 51 times independently optimized experiments show that PSO is better than DE,while the performance of the GOBLPSO algorithm is generally better than that of the PSO and OBLPSO.The median-optimized NO_(x) emission by GOBLPSO is up to 15.8 mg/m^(3) lower than that obtained by PSO.The generalized opposition-based learning can effectively utilize the information of the current search space and enhance the adaptability of PSO to the low NO_(x) combustion optimization of the studied boiler.

A Survey on Intelligent Optimization Approaches to Boiler Combustion Optimization

This paper reviews the researches on boiler combustion optimization,which is an important direction in the field of energy saving and emission reduction.Many methods have been used to deal with boiler combustion optimization,among which evolutionary computing(EC)techniques have recently gained much attention.However,the existing researches are not sufficiently focused and have not been summarized systematically.This has led to slow progress of research on boiler combustion optimization and has obstacles in the application.This paper introduces a comprehensive survey of the works of intelligent optimization algorithms in boiler combustion optimization and summarizes the contributions of different optimization algorithms.Finally,this paper discusses new research challenges and outlines future research directions,which can guide boiler combustion optimization to improve energy efficiency and reduce pollutant emission concentrations.

Investigation of the combustion deterioration of an automotive diesel engine under transient operation

With increasingly stringent emission regulations and demand for fuel economy by the public,the combustion and emission problems of automotive diesel engines during transient operation have become vital and urgent issues.In this study,combustion deterioration has been experimentally analyzed using a heavy-duty turbocharged diesel engine running under transient conditions(constant speed and increasing torque).Optimization of the transient combustion process was performed by adjusting the fuel injection parameters.The results indicated that the notable combustion deterioration relative to steady state operation while transient was a function of the delay in the air-supply to the turbocharged engine,and took the form of combustion phasing delay,resulting in rapidly increasing smoke emission and fuel consumption.However,the delay in combustion phasing can be controlled by advancing the fuel injection timing,effectively increasing thermal efficiency.Unfortunately,smoke and NO x emissions increased at the same time.The deterioration in combustion phasing can also be improved by increasing injection pressure,resulting in decreased smoke emission while NO x emission increased.It is worth noting that the effective thermal efficiency first increased and then decreased as fuel injection pressure increased during transient operation.

Cylinder pressure based combustion phase optimization and control in spark-ignited engines

Efficiency and emissions of spark-ignited engines are significantly affected by combustion phase which can usually be indicated by crank angle of 50% mass burnt （CA50）. Managing combustion phase at the optimal value at which the maximal efficiency can be achieved is a challenging issue due to the cyclic variations of combustion process. This paper addresses this issue in two loops： CA50 set-point optimization （outer loop） and set-point tracking （inner loop） by controlling spark advance （SA）. Extremum seeking approach maximizing thermal efficiency is employed in the CA50 set-point optimization. A proportional- integral （PI） controller is adopted to make the moving average value of CA50 tracking the optimal CA50 set-point determined in the outer loop. Moreover, in order to obtain fast responses at steady and transient operations, feed-forward maps are designed for extremum seeking controller and PI controller, respectively. Finally, experimental validations are conducted on a six-cylinder gasoline at steady and transient operations to show the effectiveness of proposed control scheme.

Combustion Optimization Model for NO_x Reduction with an Improved Particle Swarm Optimization

This paper focuses on the combustion optimization to cut down NO_x emission with a new strategy.Firstly, orthogonal experimental design(OED) and chaotic sequences are introduced to improve the performance of particle swarm optimization(PSO). Then, a predicting model for NO_x emission is established on support vector machine(SVM) whose parameters are optimized by the improved PSO. Afterwards, a new optimization model considering coal quantity and air quantity along with the traditional optimization variables is established. At last,the operating parameters are optimized by the improved PSO to cut down the NO_x emission. An application on 600 MW unit shows that the new optimization model can cut down NO_x emission effectively and maintain the load balance well. The NO_x emission optimized by the improved PSO is lowest among some state-of-the-art intelligent algorithms. This study can provide important guides for the low NO_x combustion in the power plant.