Deposits in thermal power plant condensers

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Abstract:

Unexpected fouling in condensers has always been one of the main operational concerns in

thermal power plants. This paper describes an approach to predict fouling deposits in thermal power plant

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condensers by means of support vector machines (SVMs). The periodic fouling formation process and

residual fouling phenomenon are analyzed. To improve the generalization performance of SVMs, an

improved differential evolution algorithm is introduced to optimize the SVMs parameters. The prediction

model based on optimized SVMs is used in a case study of 300MW thermal power station. The experiment

result shows that the proposed approach has more accurate prediction results and better dynamic

self-adaptive ability to the condenser operating conditions change than asymptotic model and T-S fuzzy

model.

Keywords: Fouling prediction; Condensers; Support vector machines; Differential evolution

1. Introduction

Condenser is one of key equipments in thermal power plant thermodynamic cycle, and its thermal

performance directly impacts the economic and safe operation of the overall plant [1]. Fouling of steam

condenser tubes is one of the most important factors affecting their thermal performance, which reduces

effectiveness and heat transfer capability with time [2, 3]. It is found that the maximum decrease in

effectiveness due to fouling is about 55 and 78% for the evaporative coolers and condensers, respectively

[2]. As a consequence, the formation of fouling in condenser of thermal power plants has special economic

significance [4-6]. Furthermore, it represents the concerns of modem society in respect of conservation of

limited resources, for the environment and the natural world, and for the improvement of industrial working

conditions [6, 7].

The fouling of heat exchangers is a wide ranging topic coveting many aspects of technology, the

designing and operating of condenser must contemplate and estimate the fouling resistance to the heat

transfer. The knowledge of the progression and mechanisms of formation of fouling will allow a design of

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an appropriate fouling mitigation strategy such as optimal cleaning schedule to be made. The most common

used models for fouling estimation are the thermal resistance method and heat transfer coefficient method

[6-10]. However, the residual fouling of periodic fouling deposition process and the dynamic changes of

heat exchanger operating condition are not considered in these models. Consequently, the estimation error

of those methods is very large.

Artificial Neural Networks (ANNs) are capable of efficiently dealing with many industrial problems

that cannot be handled with the same accuracy by other techniques. To eliminate most of the difficulties of

traditional methods,

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