Lluvia M. Ochoa-Estopier

Feasibility bounds in operational optimization and design of crude oil distillation systems using surrogate methods

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.

Area-based retrofit and operational optimization of existing crude oil heat exchanger networks

Retrofit and operational optimization projects are frequently implemented in crude oil heat exchanger networks (HENs) to reduce operating costs. However, it is a challenge to optimize these HENs because of their complexity and the many practical constraints to be considered (e.g. plant layout, installed area, limited budget, pressure drop limitations). This work presents an optimization approach for industrial crude oil preheat HENs. The approach identifies the retrofit modifications and/or operating conditions that minimize operating and retrofit capital costs. Retrofit modifications considered include adding, deleting and relocating an exchanger, and adding and deleting a stream splitter. Operational optimization variables include stream split fractions and heat transfer area. The main feature of this practical approach is the specification of heat exchangers in terms of heat transfer area. With area-based models, it is easier to monitor and constrain the heat transfer area of individual exchangers during optimization, compared to duty-based models. Furthermore, this consideration significantly simplifies the optimization, while capturing the details of the existing HEN. Temperature-dependent heat capacities are also considered. Practical constraints are implemented to ensure that industrially-relevant solutions can be achieved. These constraints include existing heat transfer area, maximum number of modifications, and forbidden relocations and matches. A case study on an industrial HEN demonstrates how the approach identifies opportunities to reduce energy consumption with minimal structural modifications.