基于 Gamma 加速积灰模型的燃煤锅炉吹灰优化

针对燃煤锅炉受热面吹灰操作不合理的问题,以吹灰最小费用为目标,提出一种基于 Gamma 加速积灰模型的吹灰优化 方法。 首先分析在锅炉受热面的积灰状态呈连续单调递增的情况下,以一个服从指数分布的连续非负的随机变量来表示锅炉 受热面积灰的速度,建立加速积灰模型;其次,将积灰过程近似成 Gamma 过程,以最小平均费用为目标,建立了锅炉受热面的吹 灰优化模型。 以某 300 MW 燃煤机组省煤器的受热面积灰状态为算例分析,利用粒子群优化算法优化吹灰平均费用率,得到锅 炉受热面最佳的吹灰次数和吹灰阈值,使得锅炉受热面吹灰的平均费用率最小,说明了所提出的吹灰最小费用率优化模型的可 行性。

Soot blowing optimization based on Gamma accelerated ash accumulation model for coal-fired boilers

Aiming at the problem of improper ash blowing operation on the heating surface of coal-fired boilers, this paper proposes a soot blowing optimization method based on Gamma acceleration soot deposition model with the aim of minimizing soot blowing cost. In this paper, firstly, in the case that the ash accumulation state on the heating surface of the boiler is continuously monotonically increasing, a continuous non-negative random variable obeying the exponential distribution is used to express the ash velocity of the boiler heating area, and an accelerated ash accumulation model is established; Secondly, under the circumstance the ash process is regarded as a similar process to the Gamma model. An optimized model for ash blowing on the heating surface of the boiler is established to minimize the average cost. Taking the ash state of the heating area of a 300 MW coal-fired economizer as an example, the PSO algorithm is used to optimize the average cost of ash blowing, obtaining the best soot blowing times and soot blowing thresholds on the heating surface of the boiler, so that making the average cost of the soot blowing on the heating surface of the boiler smallest. It also illustrates the feasibility of the minimum cost rate optimization model proposed in this paper.