HEAT EXCHANGER NETWORK RETROFITTING

Ph.DDOCTOR OF PHILOSOPH

New Strategies for Global Optimization of Chemical Engineering Applications by Differential Evolution

Ph.DDOCTOR OF PHILOSOPH

Optimization of refinery preheat trains undergoing fouling: control, cleaning scheduling, retrofit and their integration

Crude refining is one of the most energy intensive industrial operations. The large amounts of crude processed, various sources of inefficiencies and tight profit margins promote improving energy recovery. The preheat train, a large heat exchanger network, partially recovers the energy of distillation products to heat the crude, but it suffers of the deposition of material over time – fouling – deteriorating its performance. This increases the operating cost, fuel consumption, carbon emissions and may reduce the production rate of the refinery. Fouling mitigation in the preheat train is essential for a profitable long term operation of the refinery. It aims to increase energy savings, and to reduce operating costs and carbon emissions. Current alternatives to mitigate fouling are based on heuristic approaches that oversimplify the representation of the phenomena and ignore many important interactions in the system, hence they fail to fully achieve the potential energy savings. On the other hand, predictive first principle models and mathematical programming offer a comprehensive way to mitigate fouling and optimize the performance of preheat trains overcoming previous limitations. In this thesis, a novel modelling and optimization framework for heat exchanger networks under fouling is proposed, and it is based on fundamental principles. The models developed were validated against plant data and other benchmark models, and they can predict with confidence the main effect of operating variables on the hydraulic and thermal performance of the exchangers and those of the network. The optimization of the preheat train, an MINLP problem, aims to minimize the operating cost by: 1) dynamic flow distribution control, 2) cleaning scheduling and 3) network retrofit. The framework developed allows considering these decisions individually or simultaneously, although it is demonstrated that an integrated approach exploits the synergies among decision levels and can reduce further the operating cost. An efficient formulation of the model disjunctions and time representation are developed for this optimization problem, as well as efficient solution strategies. To handle the combinatorial nature of the problem and the many binary decisions, a reformulation using complementarity constraints is proposed. Various realistic case studies are used to demonstrate the general applicability and benefits of the modelling and optimization framework. This is the first time that first principle predictive models are used to optimize various types of decisions simultaneously in industrial size heat exchanger networks. The optimization framework developed is taken further to an online application in a feedback loop. A multi-loop NMPC approach is designed to optimize the flow distribution and cleaning scheduling of preheat trains over two different time scales. Within this approach, dynamic parameter estimation problems are solved at frequent intervals to update the model parameters and cope with variability and uncertainty, while predictive first principle models are used to optimize the performance of the network over a future horizon. Applying this multi-loop optimization approach to a case study of a real refinery demonstrates the importance of considering process variability on deciding about optimal fouling mitigation approaches. Uncertainty and variability have been ignored in all previous model based fouling mitigation strategies, and this novel multi-loop NMPC approach offers a solution to it so that the economic savings are enhanced. In conclusion, the models and optimization algorithms developed in this thesis have the potential to reduce the operating cost and carbon emission of refining operations by mitigating fouling. They are based on accurate models and deterministic optimization that overcome the limitations of previous applications such as poor predictability, ignoring variability and dynamics, ignoring interactions in the system, and using inappropriate tools for decision making.Open Acces

An Improved Method for Predicting Heat Exchanger Network Area

Successful application of pinch analysis to any process, be it for grassroots design or retrofit, depends upon the extent to which set targets are achieved in practice. This entails predicating the three stages of process integration namely targeting, synthesis and detailed design on the same basis. There exist gap between these three stages largely due to inaccuracies in film heat transfer coefficient and inability to replicate same at the various stages. This paper presents an improved methodology for area targeting that is consistent with detailed design of an exchanger not just because it is premised on the same basis of pressure drop constraints but, more importantly, because it allows, for necessary variation of stream properties with temperature. The validity of the methodology has been tested using two case studies from the literature. The results obtained in all studies reveal a difference of less than 2% between targeting, synthesis and detailed design with the new methodology. This is contrary to the difference of as high as 59% between targeting and detailed design obtained with the state-of-the-art methodology. There is therefore an excellent agreement between the three stages of process integration arising from the new methodology. Keywords: heat exchanger network, area targeting, synthesis, detailed design, pressure drop, film heat transfer coefficient

Energy management and guidelines to digitalisation of integrated natural gas distribution systems equipped with expander technology

In a swirling dynamic interaction, digital innovation, environment and anthropological evolution are swiftly shaping the smart grid scenario. Integration and flexibility are the keywords in this emergent picture characterised by a low carbon footprint. Digitalisation, within the natural limits imposed by the thermodynamics, seems to offer excellent opportunities for these purposes. Of course, here starts a new challenge: how digital technologies should be employed to achieve these objectives? How would we ensure a digital retrofit does not lead to a carbon emission increase? In author opinion, as long as it remains a generalised question, none answer exists: the need to contextualise the issue emerges from the variety of the characteristics of the energy systems and from their interactions with external processes. To address these points, in the first part of this research, the author presented a collection of his research contributions to the topic related to the energy management in natural gas pressure reduction station equipped with turbo expander technology. Furthermore, starting from the state of the art and the author's previous research contributions, the guidelines for the digital retrofit for a specific kind of distributed energy system, were outlined. Finally, a possible configuration of the ideal ICT architecture is extracted. This aims to achieve a higher level of coordination involving, natural gas distribution and transportation, local energy production, thermal user integration and electric vehicles charging. Finally, the barriers and the risks of a digitalisation process are critically analysed outlining in this way future research needs

Advances in optimal design and retrofit of chemical processes with uncertain parameters - Applications in design of heat exchanger networks

There is widespread consensus that the omnipresent climate crisis demands humanity to rapidly reduce global greenhouse gas (GHG) emissions.To allow for such a rapid reduction, the industrial sector as a main contributor to GHG emissions needs to take immediate actions. To mitigate GHG emissions from the industrial sector, increasing energy efficiency as well as fuel and feedstock switching, such as increased use of biomass and (green) electricity, are the options which can have most impact in the short- and medium-term.Such mitigation options usually create a need for design of new or redesign of existing processes such as the plant energy systems.The design and operation of industrial plants and processes are usually subject to uncertainty, especially in the process industry. This uncertainty can have different origins, e.g., process parameters such as flow rates or transfer coefficients may vary (uncontrolled) or may not be known exactly.This thesis proposes theoretical and methodological developments for designing and/or redesigning chemical processes which are subject to uncertain operating conditions, with a special focus on heat recovery systems such as heat exchanger networks.In this context, this thesis contributes with theoretical development in the field of deterministic flexibility analysis.More specifically, new approaches are presented to enhance the modelling of the expected uncertainty space, i.e., the space in which the uncertain parameters are expected to vary.Additionally, an approach is presented to perform (deterministic) flexibility analysis in situations when uncertain long-term development such as a switch in feedstocks interferes with operational short-term disturbances.In this context, the thesis presents an industrial case study to i) show the need for such a theoretical development, and ii) illustrate the applicability.Aside of advances in deterministic flexibility analysis, this thesis also explores the possibility to combine valuable designer input (e.g. non-quantifiable knowledge) with the efficiency of mathematical programming when addressing a design under uncertainty problem.More specifically, this thesis proposes to divide the design under uncertainty problem into a design synthesis step which allows direct input from the designer, and several subsequent steps which are summarized in a framework presented in this thesis.The proposed framework combines different approaches from the literature with the theoretical development presented in this thesis, and aims to identify the optimal design specifications which also guarantee that the the final design can operate at all expected operating conditions.The design synthesis step and the framework are decoupled from each other which allows the approach to be applied to large and complex industrial case studies with acceptable computational effort.Usage of the proposed framework is illustrated by means of an industrial case study which presents a design under uncertainty problem