Abstract
The innaccurate on process data in PLTGU Gresik does not satisfy the mass and energy balance. Data reconciliation techniques can effectively improve precision and reduce measurement error on process variable estimation of data plant through modeling and optimization techniques. In this paper, we propose PSO (Particle Swarm Optimization) algorithm to solve the data reconciliation problem for precise improvement and error minimization. As a result, the standard deviation of data measurement and reconciliation is different on each variable heat exchanger component, so that indicates random errors on measurement. Based on the result, PSO algorithm is capable generate reliable data and minimizing error with sum square error is equal to 1.153. It means PSO algorithm is compatible with the instrument system on PLTGU Gresik. Moreover, data reconciliation is applied then followed with detection gross error using statistical test that is Global Test. As the result, there is not gross error on the measurement.
Highlights
The innaccurate on process data in PLTGU Gresik does not satisfy the mass and energy balance
Data reconciliation have been widely used in the power plant [4]–[6], on gas turbines and combined cycle ower generation units [7] and coalfired generation units [8]–[10]
We will solve the steady state data reconciliation problem in more complex plant, which is consist of eight heat exchanger components that using Particle Swarm Optimization (PSO) algorithm
Summary
Accurate process data is important to evaluate the performance of process operation and to justify the process mass and energy balances [1]. Based on raw data of PLTGU Gresik, there is a inconsistent and inaccurate process which is not satisfy mass and energy balances. It can be reduced by using more accurate instrumentation, calibration, and data reconciliation. We will present data reconciliation on the power plant of heat exchanger component, which is preheater, low pressure economizer (LP Eco), high pressure economizer 1 (HP Eco 1), low pressure evaporator (LP Eva), high pressure economizer 2 (HP Eco 2), high pressure evaporator (HP Eva), high pressure superheater 1 (HP SH 1), and high pressure superheater 2 (HP SH 2) which is satisfy the mass and energy balance in steady state condition. This work will focus on data reconciliation in PLTGU Gresik using particle swarm optimization (PSO) and sequentially gross error detection. PSO is expected to solve data reconciliation problems optimally
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