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A novel planar micro-combustor with four-corner entrances is proposed by considering high and uniform outer wall temperature distribution used in micro thermo-photovoltaic generator. For this, a three-dimensional numerical model with detailed chemical mechanism, transport, and heat transfer and a thermo-photovoltaic energy conversion model are developed to examine its performance. Results show that the proposed micro-combustor has excellent wall temperature uniformity because a uniform distribution of high-temperature zones of four individual flames is formed in the combustion chamber. Thermal performance and energy efficiency of the proposed micro-combustor are better than conventional micro-combustor at a low flow rate (m) and large equivalence ratio (φ). Additionally, the implemented plate-insertion strategy effectively overcome the deterioration of combustor performance under higher m or lower φ. Proposed micro-combustor with plates insertion has the largest wall temperature and energy efficiency, no matter what φ and m are adopted. Plates extend the fluid ravel path and produce some recirculation zones, which promotes heat transfer, preferential diffusion, and flame anchoring, while its pressure loss is larger. Of all the tested cases, the system efficiency reaches a maximum of 6.39%, corresponding to 12.1 W electricity output. This work provides effective combustor design and research insights to develop high-efficiency micro-power system.

To prevent premature convergence frequently appeared in the particle swarm optimization (PSO) for the complex and highly nonlinear relationship between the system inputs and output(s), an improved particle swarm optimization was proposed, named particle swarm optimization with invasive weed (IW-PSO). In IW-PSO, PSO and IWO were integrated in parallel form based on the powerful local search ability of the invasive weed optimization (IWO), and after some iterations, the more diversiform and adaptive invasive weeds were introduced in particle swarm for improving the diversity, the worse particles comparing the fitness of the best previous position were partly mutated for assistantly local searching in invasive weeds. Simulation results show that the method has better searching ability and stability for some complex multimodal functions. Then, IW-PSO was introduced into low Nox combustion optimization, comparison results indicate that the proposed method is a fast and effective one to reduce NOx emissions.

Motor-driven and steam-driven modes are mainly driving modes of boiler feed pumps in China. A novel evaluation method based on equivalent work efficiency was put forward, and the exemplification of feed water pump driving modes of 300 MW unit showed the feasibility and effectiveness of the method. The two driving modes of boiler feed pumps were analyzed and compared by net heat consumption rate, equivalent work efficiency rate and comprehensive cost-based coal consumption rate. The results show that the use of steam-driven pumps in 300 MW and large units can reduce net heat consumption rate and increase the output of the unit. The operating stability and regulating performance of steam-driven pumps are improved, so steam-driven pumps should be superior to motor-driven pumps.

This paper first analyzed the significance and different arithmetic for exhaust enthalpy. Then the influences of relative factors on exhaust enthalpy were analyzed respectively on the foundation of energy balance theory in thermal system. A new calculation strategy was put forward, combined with the regularity in wet steam region and advantage of other arithmetic. Initial values were gained by curve extrapolation method in this new strategy, which also applied pressure ratio of last stage and efficiency of stage groups for enthalpy calculation in wet steam region. Thermal balance method was applied in iterative calculating, which centralized on last stages and reduced computational complexity. Concrete examples were given under different conditions based on one 300 MW unit in Xi Baipo power plant. And the calculation results show that new method is featured by swiftness accuracy and ease of practical application, which has important significance on real-time monitoring research work for unit performance.

To tackle the boiler combustion multiobjective optimization problem, an improved Pareto multiobjective genetic algorithm (IMOGA) is developed based on non- dominated sorting genetic algorithm � (NSGA-II). In proposed algorithm, its population classification mechanism and an outer sets container technique which contributes to maintaining diversity of the solutions and is the merit of SPEA2 is integrated. Studies on high efficiency and low NOX emissions combustion optimization were carried out by a previous purposed model of high efficiency and low NOX emissions and the IMOGA. In the comparison of IMOGA with Two-Weighted-Objective Genetic Algorithm (TWOGA), the IMOGA shows good results and can find multiple Pareto optimal solutions in one single run. The optimization results obtained by two algorithms shows that they agree well with each other in the trend of optimal solutions and that of the IMOGA is better. In the second stage of combustion modification, some optimization algorithms are used to manipulate the inputs of the model in order to minimize NOX emissions and maximize efficiency output simultaneously. So the selection and the implementation of optimization algorithms are the main task of the second stage. Evolutional Algorithms (EAs) are optimization techniques simulating biological systems which are classified as a category of the research of so-called artificial life. Such algorithms have been developed to solve large-scale optimization problems, for which traditional mathematical techniques may fail. The typical EAs include genetic algorithm (GA), particle swarm optimization (PSO), and ant colony systems (ACO) and so on. Among these optimization methods, GA is frequently used to optimize operational conditions of the boiler for NOX emissions reduction (5-8). On the basis of NOX emissions and efficiency response property model (4), multiobjective optimization model for high efficiency and low NOX emissions combustion optimization was improved in (8), then The objectives of boiler efficiency and NOX emissions concentration optimization was transformed into single-objective optimization by means of weighted sum method, furthermore, it was solved by genetic algorithm on real coding. For convenience, the optimization algorithm used in (8) is named as Two-Weighted-Objective Genetic Algorithm (TWOGA) in this paper.

Relative internal efficiency is an important economic index which reflects characteristics of flow passage equipments of turbine. This paper analyzed the concepts of relative internal efficiency and cylinder efficiency, and deduced relation-model for them. On the one hand, one new method was put forward to calculate relative internal efficiency. On the other hand, this model could also represent the influence on turbine flow efficiency and economy of cylinder efficiency. In order to certify the model, data of design-condition were used to find the law of relative coefficients, and real-time data and experiment-data were applied to certify the law and the model. Calculation results could show the validity of connection-model, which present operating condition of flow passage. Further more, with the coefficients law, this model could be used to predict economic condition of flow passage equipments and provide an academic foundation for flow passage alteration and efficiency monitoring.

In order to meet the requirement of high efficiency and low NOx emission combustion of coal-fired boiler, two constrained optimization objectives were designed based on the practical requirement, the one was that maximization of boiler efficiency under NOx emission constraint, and the other one was that minimization of NOx emission under a good boiler performance. Considering the complexity of response characteristics modeling about efficiency and NOx emission, a hybrid particle swarm optimization with invasive weed (IW-PSO) was proposed to optimize the constrained objective functions. In IW-PSO, particle swarm optimization (PSO) and invasive weed optimization (IWO) were integrated in parallel form, and after some iteration, IWO was considered to assist PSO. And in the process of optimizing, variational optimization objective values were tracked by inspection of the results conducted previously. The optimized results indicate that the proposed method can effectively control NOx emission and improve boiler efficiency for different constrained objectives.