CHEMICAL ENGINEERING TRANSACTIONS VOL. 61, 2017 A publication of The Italian Association of Chemical Engineering Online at www.aidic.it/cet Guest Editors: Petar S Varbanov, Rongxin Su, Hon Loong Lam, Xia Liu, Jiří J Klemeš Copyright © 2017, AIDIC Servizi S.r.l. ISBN 978-88-95608-51-8; ISSN 2283-9216 Feasibility Bounds in Operational Optimization and Design of Crude Oil Distillation Systems Using Surrogate Methods Megan Jobson a, *, Lluvia M. Ochoa-Estopier a , Dauda Ibrahim a , Lu Chen b , Gonzalo Guillén Gosálbez c , Jie Li a a Centre for Process Integration, School of Chemical Engineering and Analytical Science, The University of Manchester Sackville Street, Manchester M13 9PL, UK b Process Integration Limited, Station House, Stamford New Road, Altrincham, Cheshire WA14 1EP, UK cCentre for Process Systems Engineering, Imperial College of Science, Technology and Medicine, London SW7 2BY, UK megan.jobson@manchester.ac.uk Crude oil distillation systems, comprising distillation units and their associated heat recovery networks, are highly complex and integrated systems. Their function is to separate crude oil into several streams with different boiling ranges for downstream processing. In practice, these systems typically need to be operated efficiently, so that the value added by the separation units is maximized (e.g. by maximizing flows of the most valuable intermediate products while minimizing production costs). Process improvement projects typically seek to increase production and/or to reduce energy consumption in existing crude oil distillation systems. Recent developments in design and operational optimization of crude oil distillation systems apply surrogate models, together with stochastic optimization techniques, for column design or operational optimization. Column operation is highly constrained by the product specifications and, in existing columns, by physical limitations related to column configuration and size. Column models must capture these constraints. The effectiveness of surrogate modelling of the columns is enhanced by this work that develops complementary screening and filtering correlations and surrogate models (using artificial neural networks and support vector machines) to define feasibility bounds. Applying these feasibility bounds enables more targeted searches, bringing robustness and efficiency to the optimization frameworks. Examples and case studies illustrate the effectiveness of the correlations and surrogate models for defining constraints in design and operational optimization approaches. 1. Introduction The importance of distillation in the chemical process industries is well known. Methods for column design are established, but challenges remain for distillation retrofit and operational optimization. Design, retrofit and operational optimization problems have different features related to objectives and constraints, which has implications for modelling and optimization. Models need to be sufficiently rigorous and versatile to describe real processes, whether for design or simulation. The effectiveness of optimizations applying these models depends on the complexity and accuracy of the model, on the optimization algorithm and on the definition of optimization bounds. A wide search space can facilitate global optimization, but may include many infeasible solutions, which can undermine the optimization search. Crude oil distillation is an essential, capital- and energy-intensive step in petroleum refining that presents particular challenges for optimization-based process design. Challenges include the complexity of the multicomponent mixture, the complex column configuration, the interactions with the heat recovery system and the many degrees of freedom for design and operation. Constraints relate to product quality (defined e.g. in terms of boiling properties) and to equipment limitations (e.g. column hydraulics). These challenges and constraints motivate ongoing research to develop effective and robust new methodologies, considering feasibility bounds and practical constraints, with potential impact on engineering costs, product yield and production margins, energy demand and environmental impact, especially greenhouse gas emissions. DOI: 10.3303/CET1761306 Please cite this article as: Jobson M., Ochoa-Estopier L.M., Ibrahim D., Chen L., Gosálbez G.G., Li J., 2017, Feasibility bounds in operational optimization and design of crude oil distillation systems using surrogate methods, Chemical Engineering Transactions, 61, 1849-1854 DOI:10.3303/CET1761306 1849