Process design methods for sustainable energy systems

Today, energy is the driver of the modern society, allowing for transforming raw materials and resources into ``useful'' products and services. Concerns on climate change and resources scarcity together with the increasing importance put on the environmental impacts are redefining the problem of the energy usage in the society. In this context, developing a sustainable energy systems means designing the system that will convert in an optimal way the available resources into useful products and services, reaching the maximization of the resources productivity and minimizing its environmental impact. In a given socio-economical and environmental context, the goal of the process design is to select the type and the size of the technologies, the way they are interconnected and the way they are operated. A computer aided methodology and the corresponding process system engineering tools for the analysis and the synthesis of efficient processes and energy conversion systems will be presented. It combines the use of thermo-economic and environomic models of sub-systems that are structured in an equipment data base. These models include the representation of the possible interfaces that a technology will present when it is integrated to build the integrated system. A systematic method based on process integration and optimization techniques is then used to generate process superstructure and process configurations. Models are then used to calculate the system performances in terms of thermodynamic performances, operating and investment cost together with Life cycle impact assessment indicators. Multi-objective optimization techniques are then used to decide the best values of the design decision variables and to generate thermo-economic Pareto sets of process configurations where the trade-off between economical criteria and thermodynamical or environmental impact criteria are represented. Such tools are used to support decisions in the process engineering work. In this iterative procedure, one has to recognize that the problem is never perfectly defined at the beginning of the project. The methodology includes therefore solutions analysis tools such as exergy analysis and sensitivity analysis and graphical representations that are used to understand the energetics of the solutions and refine the process design problem definition. The tools developed are applied in different fields : rational use of energy, water and waste management in the industrial processes, energy conversion systems design like power plants with $CO_{2}$ capture, fuel cell systems and biofuels production and in the study of urban energy systems. In the presentation, examples of the biofuel production process design will be used to illustrates the different steps of the methodology and demonstrate the role of process design in the development of sustainable energy systems.

Méthodologie de la synthèse des systèmes énergétiques industriels

Raffaele Bolliger

This work presents a synthesis method that leads to the preliminary design of industrial energy systems. Such systems are composed of several technologies that transform, through a set of physical unit operations, raw materials and energy into products and energy services. The purpose of the preliminary synthesis is to define which technologies form the system, to calculate their technical characteristics, their operating conditions and their interactions, based on performance indicators such as for example the cost of the system or its environmental impact. This area is studied since a long time and many synthesis approaches are available. However, these methods have significant limitations, particularly in their ability to face the study of more and more complex systems. The main goal of the method presented in this work is to enable the study of large systems and promote the use of the experience gained from previous studies in the form of models and methodological approaches. The synthesis method of industrial energy systems is based on the overall system optimization using a model that is obtained by assembling several modules, representing each a set of physical unit operations and whose equations are formulated with the black box technique, and an integration model that can represent the possible interactions within the system. Optimization is performed using a multi-objective evolutionary algorithm, whose objective functions are defined on the basis of calculation of several performance indicators based on the model of the system. To simplify the resolution of this complex system, the optimization problem is decomposed into a master problem, responsible for calculating the characteristics of the units and their operating conditions, and an optimization slave subproblem, which selects the units being part of the system and their interconnections. To ensure its robustness, the slave subproblem is formulated as a mixed-integer linear optimization problem. The slave subproblem is formulated by using process integration techniques, which are extended in this work to allow the synthesis of multiple heat and mass transfer networks. The synthesis problem can thus be defined using an explicit superstructure or by one generated automatically, either implicit or explicit. This work also introduces a technique that greatly reduces the number of degrees of freedom of the integration model. Instead of separately optimizing each ΔTmin/2 associated with heat streams, a formula is applied to calculate their value from a reference case. The ΔTmin/2 optimization is thus reduced to the optimization of a single decision variable related to the reference case, regardless of the size of the problem. The proposed method uses a set of heterogeneous elements, including flowsheeting software for modeling physical unit operations, mathematical programming tools for the formulation of the integration problem, methods for calculating performance indicators and calculation tools such as the optimization algorithm. This work introduces new tools developed to systematically apply the proposed methodology and to automate recurring operations of data transfer and the call of the various used software. In particular, a syntax description is defined as an abstraction layer to describe and to structure the exchanged information. A computing platform has been created to support the application of the method and to ensure the data transfer between its components. Two case studies are presented to illustrate the various aspects of the synthesis method. A first case, involving the synthesis of two combined cycles, has been chosen to illustrate the different application stages of the method and to show the potential reuse of certain modules. Through the integration techniques, it has been possible to identify potential heat recovery that can increase the performance of one of the cycles beyond what had been expected by experts using conventional simulation techniques. The second case study is about the treatment of waste generated by an industrial site active in the field of fine chemicals. Waste treatment can recover different materials and energy services useful for process units, thereby reducing the quantities purchased in the market. The model of multi-network integration can easily solve the complexity of the problem of waste management in developing strategies for allocating waste to the various treatments available for different objective functions related to operating costs and environmental impact.

EPFL2010

Urban Energy System Design from the Heat Perspective using mathematical Programming including thermal Storage

Jakob Moritz Fabian Rager

Energy planning recently received more attention in Switzerland through the new strategy phasing out nuclear energy by 2034. Often however the energy planning is only done from the electrical side. This work takes a different angel and helps communities and energy utilities planning tomorrows energy system from a heat based perspective. After the data collection and structuring, the methodology presented here designs an energy system. Based on the quality of the collected data, the approach to define the energy demand should be chosen. In order to reduce calculation time, a data reduction approach is developed to reduce the input data without loosing significant information and precision. In particular, the methodology focuses on the integration of a stochastic resource, in this case solar thermal heat production, in combination with thermal energy storage. The thermal energy storage can be used as a short or long term thermal energy storage. The framework compares design solutions for the two storage types considering either a total cost approach or a life cycle assessment approach using the cumulative exergy demand (CExD). The proposed mathematical programming framework is based on a mixed integer linear programming (MILP) approach, that can work on different levels of detail between building to community or city level. The optimization problem can also be further simplified to a linear problem, increasing the size of problem that can be solved while reducing or keeping a constant computation time. The discussed cases show an interest in further investigating storage solution using both, the short and long term storage at once, because they allow to reduce the system's overall costs or CExD significantly. The framework is then extended to consider buildings as an energy storage. The building's internal temperature can be raised from 20 \degree C up to 23 \degree C, giving a comfort temperature range that can be used for storing heat. The integrating of both storage types, the thermal energy storage and the building as an energy storage, show no significant impact on the energy system design. However, costs or CExD can be reduced. In addition, the heat demand can be modified through the decision of optimal energy retrofitting strategy for a group of buildings. The framework decides which of the building to refurbish based the overall CExD including CExD used for retrofitting the building. Finally, a method is proposed to integrate uncertainty of the model's input parameters into the system design. A global sensitivity analysis evaluates the impact of each uncertain parameter onto the system, allowing to focusing on the outputs of interest. Robust optimization is applied with a simulation-based approach, the additional costs for a robust design are calculated, as well as the different unit sizes. The low complexity of the developed models allows for an easy integration of new data collected during the development of a project, which is often the case in urban energy planning applications.

EPFL2015

Optimal design of solar-assisted industrial processes considering heat pumping: Case study of a dairy

Ivan Daniel Kantor, Maziar Kermani, François Maréchal, Régis Voillat, Anna Sophia Wallerand

Pinch analysis and Mixed Integer Linear Programming (MILP) have been extensively studied for optimization of industrial processes addressing heat recovery, utility selection and sizing. Analysis of renewable utility integration, such as solar thermal or photovoltaics, introduces several obstacles for established methods: the time-dependency of resources, storage inertia and losses, and intrinsic non-linearities of the system performance are difficult to represent by linearized, time-invariant MILP equations. Moreover, waste heat recovery options such as heat pumping cannot be neglected as a potential competitor to solar heat. This work presents a set of multi-period MILP equations for solar technologies as well as a superstructure for optimization of heat pump cycles. Additionally, a methodology is proposed and applied to simultaneously optimize the process' refrigeration and renewable utility system using ɛ-constrained parametric optimization. The proposed methodology is illustrated on the basis of a dairy plant for which the different utility technologies are compared and evaluated based on economic and environmental criteria. It is illustrated that integration of solar energy can contribute to strongly reduce the environmental impact of the process (65–75% reduction in CO2 equivalent emissions), but only in combination with heat recovery (27%) and an improved heat pump system (33%). Heat recovery and heat pump placement for industrial processes are hereby shown to reduce exergy destruction and total cost while improving system energy efficiency by means of thermo-economic optimization. The solutions show that investment in solar energy can be economically and environmentally attractive for industrial processes by considering the whole system and ensuring that solar energy is optimally integrated and utilized.

2018