The paper discusses the multifaceted problem of reconciling, inferring, and coapting online some measures of industrial plants that are either not measurable or their measure is somehow unreliable. The renewed interest on these topics is due to the incoming environmental regulations for many countries of European Community, which impose limitations on emissions for each single stack, rather than for the overall plant. The main difficulty is that the solution is to be the most possible accurate and, at the same time, to be promptly provided. It unavoidably requires the simultaneous use of (i) detailed simulations to have accurate previsions; (ii) adaptive parameters to set the simulation according to the current plant conditions; (iii) robust optimizers to detect gross errors within the set of raw data coming from the field at each sampling time; (iv) efficient solvers to ensure the online effectiveness. In addition, overall performances of the proposed solution accounts for the type of license of the commercial packages involved (process simulators) since computational times significantly differ according to the license in use.

Online Software Solution for Accurate and Reliable Estimation of Stack Emissions in Refinery Processes

MANENTI, FLAVIO;
2010

Abstract

The paper discusses the multifaceted problem of reconciling, inferring, and coapting online some measures of industrial plants that are either not measurable or their measure is somehow unreliable. The renewed interest on these topics is due to the incoming environmental regulations for many countries of European Community, which impose limitations on emissions for each single stack, rather than for the overall plant. The main difficulty is that the solution is to be the most possible accurate and, at the same time, to be promptly provided. It unavoidably requires the simultaneous use of (i) detailed simulations to have accurate previsions; (ii) adaptive parameters to set the simulation according to the current plant conditions; (iii) robust optimizers to detect gross errors within the set of raw data coming from the field at each sampling time; (iv) efficient solvers to ensure the online effectiveness. In addition, overall performances of the proposed solution accounts for the type of license of the commercial packages involved (process simulators) since computational times significantly differ according to the license in use.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/579675
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