Data reconciliation with gross error detection by NLP for heat exchanger systems

Abstract

The measured data from instruments in process control activities usually consist of random and gross errors which reduce reliability of measurement. Data reconciliation (DR) technique is applied to improve the accuracy of measured data and satisfy the law of conservation. Moreover, if data contains bias or gross errors in the system. DR is not as accurate as expected. This work performed DR with gross error detection (GED) technique to improve the data measurement of a simulated hot-oil heat exchanger and utility heat exchanger network. There are two kinds of GED: the conventional GED method and the traditional measurement test modified by NLP. The gross errors or bias in some measured data, including volumetric flow rates, supply and target temperatures of hot and cold process streams and overall heat transfer coefficient were generated. The DR with GED using NLP was done by commercial optimization software. GAMS, with a least-square objective function. The conventional GED and conventional gross error elimination applied statistical methods: basic global test and basic measurement test, respectively. The DR with GED technique produced more accurate estimates of process variables showing reductions in standard deviation. The other method, the modified measurement test, was studied for performance comparison. The performance of the modified measurement test using NLP was significantly better than one of the conventional method, in terms of the performance measures evaluation using the overall power (OP).