Cryogenic heat exchangers for process cooling and renewable energy storage: A review

© 2019 The cryogenic industry has experienced remarkable expansion in recent years. Cryogenic technologies are commonly used for industrial processes, such as air separation and natural gas liquefaction. Another recently proposed and tested cryogenic application is Liquid Air Energy Storage (LAES). This technology allows for large-scale long-duration storage of renewable energy in the power grid. One major advantage over alternative storage techniques is the possibility of efficient integration with important industrial processes, e.g., refrigerated warehousing of food and pharmaceuticals. Heat exchangers are among the most important components determining the energy efficiency of cryogenic systems. They also constitute the necessary interface between a LAES system and the industrial process utilizing the available cooling effect. The present review aims to familiarise energy professionals and stakeholders with the latest achievements, innovations, and trends in the field of cryogenic heat exchangers, with particular emphasis on their applications to LAES systems employing renewable energy resources. Important innovations in coil-wound and plate-fin heat exchanger design and simulation methods are reviewed among others, while special attention is given to regenerators as a prospective component of cryogenic energy storage systems. This review also reveals that the geographical spread of research and development activities has recently expanded from well-established centers of excellence to rather active emerging establishments around the globe

Synthesis of optimal heat and mass exchange networks using a two-step hybrid approach including detailed unit designs

This PhD thesis develops a methodology for the synthesis of optimal heat and mass exchanger networks through a novel hybrid method. The two-step procedure makes use of simplified exchanger models in a network optimisation step, followed by a detailed design where the exchangers found in the network synthesis step are modelled in detail. Subsequent iterations of the network design step are then updated with information from the detailed network designs. The algorithm has certain advantages over previous methods in that the network optimisation is based on more realistic representations of the actual units therein and also that the method increases the likelihood of attaining a globally optimal solution through the generation and assessment of multiple candidate networks throughout the algorithm. The method can be used in a variety of applications and is demonstrated to be effective for large problems and multi-period scenarios. The thesis also shows that the method can be used in conjunction with multiple individual unit optimisation techniques including heuristics and fully explicit optimisation methods

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

Development of a techno-economic energy model for low carbon business parks

To mitigate climate destabilisation, global emissions of human-induced greenhouse gases urgently need to be reduced, to be nearly zeroed at the end of the century. Clear targets are set at European level for the reduction of greenhouse gas emissions and primary energy consumption and for the integration of renewable energy. Carbon dioxide emissions from fossil fuel combustion in the industry and energy sectors account for a major share of greenhouse gas emissions. Hence, a low carbon shift in industrial and business park energy systems is called for. Low carbon business parks minimise energy-related carbon dioxide emissions by enhanced energy efficiency, heat recovery in and between companies, maximal exploitation of local renewable energy production, and energy storage, combined in a collective energy system. Moreover, companies with complementary energy profiles are clustered to exploit energy synergies. The design of low carbon energy systems is facilitated using the holistic approach of techno-economic energy models. These models take into account the complex interactions between the components of an energy system and assist in determining an optimal trade-off between energetic, economic and environmental performances. In this work, existing energy model classifications are scanned for adequate model characteristics and accordingly, a confined number of energy models are selected and described. Subsequently, a practical categorisation is proposed, existing of energy system evolution, optimisation, simulation, accounting and integration models, while key model features are compared. Next, essential features for modelling energy systems at business park scale are identified: As a first key feature, a superstructure-based optimisation approach avoids the need for a priori decisions on the system’s configuration, since a mathematical algorithm automatically identifies the optimal configuration in a superstructure that embeds all feasible configurations. Secondly, the representation of time needs to incorporate sufficient temporal detail to capture important characteristics and peaks in time-varying energy demands, energy prices and operation conditions of energy conversion technologies. Thirdly, energy technologies need to be accurately represented at equipment unit level by incorporating part-load operation and investment cost subject to economy of scale in the model formulation. In addition, the benefits of installing multiple units per technology must be considered. A generic model formulation of technology models facilitates the introduction of new technology types. As a fourth important feature, the potential of thermodynamically feasible heat exchange between thermal processes needs to be exploited, while optimally integrating energy technologies to fulfil remaining thermal demands. For this purpose, thermal streams need to be represented by heat –temperature profiles. Moreover, restrictions to direct heat exchange between process streams need to be taken into account. Finally, the possibility for energy storage needs to be included to enhance the integration of non-dispatchable renewable energy technologies and to bridge any asynchrony between cooling and heating demands. Starting from these essential features, a techno-economic optimisation model (Syn-E-Sys), is developed customised for the design of low carbon energy systems on business park scale. The model comprises two sequential stages. In the first stage, heat recovery within the system is maximised, while energy supply and energy storage technologies are optimally integrated and designed to fulfil remaining energy requirements at minimum total annualised costs. Predefined variations in thermal and electrical energy demand and supply are taken into account, next to a carbon emission cap. At the same time, heat networks can be deployed to transfer heat between separate parts of the system. In the second stage, the model generates an optimal multi-period heat exchanger network enabling all required heat exchanges. Syn-E-Sys builds upon a multi-period energy integration model that can deal with restrictions in heat exchange. It is combined with a generic technology model, that features part-load operation as well as investment cost subject to economy of scale, and a generic energy storage model. The technology model can be manipulated to represent various thermal or electrical energy conversion technology units, and serves as a building block to model more complex technologies. The storage model covers electrical as well as thermal energy storage, taking into account the effect of hourly energy losses on the storage level, without increasing the number of time steps to be analysed. For this purpose, time sequence is introduced by dividing the year into a set of time slices and assigning them to a hierarchical time structure. In addition, a more complex model for storage of sensible heat is integrated, consisting of a stack of interconnected virtual tanks. To enable the optimisation of the number of units per technology in the energy system configuration, an automated superstructure expansion procedure is incorporated. Heat transfer unit envelope curves are calculated to facilitate the choice of appropriate temperature levels for heat networks. Heat networks that are embedded within this envelope, completely avoid the increase in energy requirements that would result from the heat exchange restrictions between separated parts of the energy system. Finally, the heat exchanger network is automatically generated using a multi-period stage-wise superstructure. Two problems inherent to the heat cascade formulation are encountered during model development. As a first issue, heat networks can form self-sustaining energy loops if their hot and cold streams are not completely embedded within the envelope. This phenomenon is referred to in this work as phantom heat. As a second issue, the heat cascade formulation does not prevent that a thermal storage releases its heat to a cooling technology. To demonstrate the specific features of Syn-E-Sys and its holistic approach towards the synthesis of low carbon energy systems, the model is applied to a generic case study and to a case study from literature. The generic case study is set up to demonstrate the design of an energy system including non-dispatchable renewable energy and energy storage, subject to a carbon emission cap. For this purpose, the year is subdivided into a set of empirically defined time slices that are connected to a hierarchical time structure composed of seasons, daytypes and intra-daily time segments. The results obtained by Syn-E-Sys show a complex interaction between energy supply, energy storage and energy import/export to fulfil energy demands, while keeping carbon emissions below the predefined cap. The model enables optimisation of the intra-annual charge pattern and the capacity of thermal and electrical storage. Moreover, an optimal heat exchanger network is automatically generated. In the second case study, heat recovery is optimised for a drying process in the paper industry. To avoid the energy penalty due to heat exchange restrictions between two separated process parts, heat transfer units need to be optimally integrated. Firstly, a simplified version of the original problem is set up in Syn-E-Sys and the obtained results correspond well to literature. Subsequently, the original problem is extended to demonstrate the optimal integration of heat transfer units in a multi-period situation. In conclusion, Syn-E-Sys facilitates optimal design of low carbon energy systems on business park scale, taking into account the complex time-varying interactions between thermal and electrical energy demand, supply and storage, while the potential for heat recovery is fully exploited

MATLAB

This excellent book represents the final part of three-volumes regarding MATLAB-based applications in almost every branch of science. The book consists of 19 excellent, insightful articles and the readers will find the results very useful to their work. In particular, the book consists of three parts, the first one is devoted to mathematical methods in the applied sciences by using MATLAB, the second is devoted to MATLAB applications of general interest and the third one discusses MATLAB for educational purposes. This collection of high quality articles, refers to a large range of professional fields and can be used for science as well as for various educational purposes

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