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- Title
PERFORMANCE OF CONDENSATE-VACUUM-AND-EXTRACTION PUMP OF BSP WITH PROCESS OPTIMIZATION AND OPTIMUM NEURAL NETWORK BASED LEARNING SYSTEM.
- Authors
KARMAKAR, SANJEEV; BISWAL, GYAN RANJAN
- Abstract
Surface condenser of a power generation plant experiences maximum loss in thermal efficiency (in terms of electrical power output), typically more than 40% of the total generation capacity. The manuscript brings a novel Control and Instrumentation (C&I) approach to enhance the performance of surface condensing unit along with the design issues. This paper presents a control algorithm to get better performance of surface condensation section. The system is designed for maintaining levels of the optimal selection of condensate vacuum pump and condensate extraction pump to minimize the pressure loss involved with the system/section. The work includes a comparison between all the systems presented for system reliability. The 3-parameters Weibull distribution function is uniquely considered for evaluating the design issues of condenser module. It includes the location parameter for identification of faults and execution of operation in real-time. In addition, a process learning system is developed to enhance the performance supervision of the surface condenser in real-time. Effectiveness of the proposed model is validated on real-time automation platform based on specifications of IEEE C37.1-2007, IEC 61131-3 and IEEE 1413-2010.
- Subjects
INSTRUCTIONAL systems; NEURAL circuitry; CONDENSATE pumps; EXTRACTION techniques; RELIABILITY in engineering
- Publication
I-Manager's Journal on Power Systems Engineering, 2018, Vol 6, Issue 3, p15
- ISSN
2321-7499
- Publication type
Article
- DOI
10.26634/jps.6.3.15305