Yukinori Katagiri

An Experimental and Analytical Study on the Advanced Humid Air Turbine System

The Advanced Humid Air Turbine (AHAT) is a regenerative cycle using high-humidity air. This system improves the gas turbine thermal efficiency by using high-humidity air without needing high firing temperature and pressure ratio. It is estimated AHAT cycle thermal efficiency exceeds that of combined cycle if it is designed by the optimum conditions, and the efficiency difference grows especially by the small and medium-size gas turbine. To verify the system concept and cycle performance of AHAT system, AHAT verification plant construction began in April 2005 and completed in September 2006. The plant that consists of a gas turbine with a two-stage radial compressor (pressure ratio of 8), a two-stage axial turbine, a reverse-flow type of single-can combustor, a recuperator, a humidification tower, a water recovery tower, an economizer, and other components. It is planned to validate performance and reliability of the AHAT system. Expected performance is: rated output 3.6 MW, efficiency 43% (LHV), and NOx emissions less than 10 ppm at 16% O2. This paper describes the system verification plant constructed, a news flash of integrated test results, and so on.

A Search Space Reduction Method for Optimizing Sequential Control by Hypothetically Achievable Bound Estimation of the Objective Function

Metaheuristic methods such as genetic algorithm, simulated annealing, and artificial bee colony algorithm methods take much time to obtain an optimal solution, particularly when a large scale simulator is employed for estimating the state of the environment.In this paper, a search space reduction method for accelerating the optimization of sequential control systems is proposed. The proposed method estimates a hypothetical achievable bound of the objective function and uses it as the prior knowledge to reduce the search space. The hypothetical achievable bound is estimated using the fact that large scale plants consisting of multiple components are in many cases controlled in a sequential manner.The size of the search space reduction obtained by the proposed method is evaluated by an example problem that minimizes the start-up time of a thermal power plant. As a result, the size of the search space is reduced by 65%. The proposed method does not lose the optimality of the optimization method to be accelerated. In addition, this method is also applicable to optimization problems other than sequential control if the hypothetical achievable bound of the objective function is estimable without measuring the state of the environment or using the simulator.Copyright