Speaker: Jose Loyola Fuentes

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Extracting useful insights from raw plant data is often a challenging and frustrating experience. On one hand, measurements may be subject to random errors (e.g. arbitrary fluctuations in the environment, signal transmission) and/or systematic errors (measurement bias, instrument degradation, data corruption or complete sensor failure). On the other hand, plant operators rely on these measurements (e.g. flow rates, temperatures, pressures) for process/asset monitoring and make important operating and maintenance decisions. It is therefore clear that rigorous approaches capable of dealing with the various sources of error are needed.

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The field of statistics provides a substantial number of filtering techniques that are able to identify significant disturbances in the measured data, but the physical context where the process states are embedded in is not entirely considered. Therefore, suitable methods that combine physical and statistical knowledge of a specific system could be beneficial for monitoring purposes. Data reconciliation (DR) and gross error detection (GED) are complementary techniques that fit this purpose. While DR reduces the effect of random errors via measured variables and a process model, GED allows for the detection, identification, estimation and compensation of systematic errors. The estimations of process variables via DR are expected to be unbiased, more accurate and in accordance with the process model, which in case of heat exchangers, could be mass and energy conservation equations. Throughout the past 50 years, a wide variety of methods have been developed to address different DR and GED problems.

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In this presentation, we review current practice and state-of-the-art methods for DR and GED, applied to heat exchanger networks. The review includes considerations for both steady-state and dynamic systems and an application-oriented summary of the advantages and disadvantages of various techniques (e.g. classic and robust statistics, Kalman filtering, dynamic programming) and presents relevant case studies.

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Speaker: Francesco Coletti

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Traditional heat exchanger design and monitoring methodologies rely on the use of fixed fouling factors to account for the reduced thermal performance of heat exchangers operating in non-clean services.

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This practice has been criticised in the past as it does not account for the dependency of the underlying fouling mechanisms on process conditions and time, often leading to oversized equipment that exacerbates fouling. Neglecting fouling dynamics and its dependence on process conditions at the design stage has also been identified as the cause of major failures. In recognition of these limitations, much research effort has been put in recent years to improve the fundamental understanding of the various fouling mechanisms and industry has started shifting to a different paradigm which involves the use of system-specific models to account for the fouling dynamics at the design stage.

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This webinar will point out severe limitations of the fouling factor approach and will propose novel methodologies for the design and predictive maintenance of heat exchanger networks. These methods leverage the combination of big data, advanced analytics and multi-scale modelling to improve design, optimise operations, maximise production and minimise maintenance, resulting in significant economic and environmental benefits. Industrial case studies will be shown to illustrate methodology and benefits.

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