Optimisation of the resource efficiency in an industrial evaporation system

Abstract This work deals with the problem of resource efficiency monitoring in a multiple-effect evaporation process. The approach considers first a grey-box nonlinear stationary model of the process and data-reconciliation methods to compute efficiency indicators and to update such model if necessary. The updated model is used in a real-time optimisation layer to compute operation points of the process. Then, some patterns for optimal operation have been identified and implemented by a self-optimising controller to drive the process. In a second step, the fouling, which reduce the heat-transfer efficiency in heat exchangers, is also considered as a function of time and control decisions, in order to optimally drive the process considering the long-term effect. This fouling behaviour forces periodic stops for cleaning, which will be also object of optimisation. Finally, the overall problem is formulated as a real-time optimisation to search for the optimal decisions during a whole operation cycle.

[1]  Amgad Elgowainy,et al.  A comparative assessment of resource efficiency in petroleum refining , 2015 .

[2]  Xiang Li,et al.  Module-oriented automatic differentiation in chemical process systems optimization , 2004, Comput. Chem. Eng..

[3]  Kenichi Fujii,et al.  Revised formula for the density of moist air (CIPM-2007) , 2008 .

[4]  Yu. V. Mamonov,et al.  Expressions for thermophysical properties of superheated steam , 1999 .

[5]  Vassilios S. Vassiliadis,et al.  Scheduling the cleaning actions for a fouled heat exchanger subject to ageing: MINLP formulation , 2012, Comput. Chem. Eng..

[6]  Lorenz T. Biegler,et al.  On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming , 2006, Math. Program..

[7]  Jose Luis Pitarch,et al.  Efficient Modelling and Real-Time Optimisation of Stationary Systems: Application to an Evaporation Process , 2015 .

[8]  M. Dambrine,et al.  Fouling detection in a heat exchanger: A polynomial fuzzy observer approach , 2013 .

[9]  L. Lasdon,et al.  Efficient data reconciliation and estimation for dynamic processes using nonlinear programming techniques , 1992 .

[10]  John J.J. Chen.  Comments on improvements on a replacement for the logarithmic mean , 1987 .

[11]  R. Russell Rhinehart,et al.  Automated steady and transient state identification in noisy processes , 2013, 2013 American Control Conference.

[12]  Jeffrey B. Dahmus,et al.  Can Efficiency Improvements Reduce Resource Consumption? , 2014 .

[13]  D. Sales,et al.  Model for fouling deposition on power plant steam condensers cooled with seawater: Effect of water velocity and tube material , 2007 .

[14]  P. Tsilingiris.  Thermophysical and transport properties of humid air at temperature range between 0 and 100 °C , 2008 .

[15]  Ian K. Craig,et al.  Modelling of a dual circuit induced draft cooling water system for control and optimisation purposes , 2015 .

[16]  Sebastian Engell,et al.  Multi-stage Nonlinear Model Predictive Control with verified robust constraint satisfaction , 2014, 53rd IEEE Conference on Decision and Control.

[17]  James V. Burke,et al.  Algorithmic Differentiation of Implicit Functions and Optimal Values , 2008 .

[18]  Sigurd Skogestad,et al.  Self-optimizing control: the missing link between steady-state optimization and control , 2000 .

[19]  Sebastian Engell,et al.  Real-time resource efficiency Indicators , 2016 .

[20]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[21]  W. Rey.  Introduction to Robust and Quasi-Robust Statistical Methods , 1983 .

[22]  Mostafa M. Awad.  Influence of Surface Temperature on Surface Fouling-Theoretical Approach , 2012 .

[23]  L. G. van Willigenburg,et al.  Optimal adaptive scheduling and control of beer membrane filtration , 2015 .

[24]  David Parmenter,et al.  Key Performance Indicators: Developing, Implementing,and Using Winning KPIs , 2007 .