Model free real-time optimization for vapor compression systems

A vapor compression system's optimal input settings vary according to changes in environmental conditions. Tracking these optimal input trajectories can be challenging when insufficient information for a reliable system model is available. An alternative set of optimization approaches use system measurements. This thesis focuses on one such approach, extremum seeking control, which uses performance index measurements to determine optimal system settings. Forgoing system model knowledge and relying exclusively on data allows an optimization approach to function well on many different plants. However, this added adaptivity comes at a performance cost. Using prior system model knowledge can be helpful for ensuring that a controller design works from the start of operation and inputs can be changed as soon as information about environmental conditions is updated. By contrast, data based methods may require the control designer to spend a time generating data in order to obtain enough information about the system to make good decisions online. A central theme of this work is addressing the trade off between using prior system model knowledge and ensuring sufficient adaptability of the extremum seeking optimization approach. Two main factors in the extremum seeking design are considered: the choice of extremum seeking control law and the choice of extremum seeking control input. Extremum seeking control laws come from the field of mathematical optimization; this thesis considers the pros and cons of choosing between gradient descent and Newton descent. Both simulations and experimental results show that while Newton descent extremum seeking is less reliant on model knowledge, but slower to find optimal inputs than gradient descent extremum seeking. Because of extremum seeking's adaptability to different plants, many different inputs can be chosen for implementation. However, using an approach known as self-optimizing control, knowledge about the plant's behavior can help choose set points with optimal values that are insensitive to changes in environmental conditions. Finding these special inputs turns the input tracking problem into a regulation problem. Both simulation and experimental results confirm that combining self-optimizing control and extremum seeking control can help improve tracking even as environmental conditions change

Self-optimizing control – A survey

Self-optimizing control is a strategy for selecting controlled variables. It is distinguished by the fact that an economic objective function is adopted as a selection criterion. The aim is to systematically select the controlled variables such that by controlling them at constant setpoints, the impact of uncertain and varying disturbances on the economic optimality is minimized. If a selection leads to an acceptable economic loss compared to perfectly optimal operation then the chosen control structure is referred to as “self-optimizing”. In this comprehensive survey on methods for finding self-optimizing controlled variables we summarize the progress made during the last fifteen years. In particular, we present brute-force methods, local methods based on linearization, data and regression based methods, and methods for finding nonlinear controlled variables for polynomial systems. We also discuss important related topics such as handling changing active constraints. Finally, we point out open problems and directions for future research

Characterization and Control of Multi-Cylinder Partially Premixed Combustion

In the last decade diesel combustion has developed in a new direction. Research has been carried out trying to prolong the ignition delay and enhance fuel/air premixing to avoid diffusion combustion as well as lowering the combustion temperature through use of EGR. One of these new combustion concepts is Partially Premixed Combustion (PPC). PPC is aimed to provide combustion with low smoke and NOx without sacrificing fuel consumption. This thesis presents the development of a multi-cylinder PPC concept. It reaches from the basic characterization of this new combustion strategy to the demands on hardware, control and fuels for a realizable PPC solution. In summary it contains a thorough PPC characterization where the results suggest that high EGR, early injection PPC strategies are to prefer over late injection approaches or smokeless rich diesel combustion. Further, a strong connection between mixing period, defined as the period between end of injection and start of combustion, and PPC has been ascertained. Based on this knowledge a combustion controller with feedback control of mixing period was derived. The operating range of multi-cylinder diesel PPC was then evaluated. The study showed that the PPC load range was limited covering only 25% of the operating region for conventional combustion. In order to reach higher loads for PPC the EGR system was rebuilt to a low pressure system. This system improves EGR/air mixing and cooling and enables high EGR and boost pressure simultaneously. Additionally, gasoline fuels were introduced to extend the ignition delay and mitigate soot formation. An extensive fuel comparison was carried out to find the most suitable fuel for PPC operation. With the improved set-up the operating range was reevaluated. By combining the use of a low pressure EGR system and standard gasoline the operating region of PPC has been extended to cover 50% of the engine nominal operating region. The final part of this thesis is dedicated to a novel method of cylinder individual efficiency estimation based on the cylinder pressure trace. With this method, control strategies aiming directly at fuel consumption optimization can be developed. An extremum seeking control algorithm was applied. The results show that the controller manages to find the maximum brake torque region both with and without external excitation. Finally, the estimation error in accumulated fuel consumption from the experiments is around 1% which shows the potential of using the absolute value of the efficiency estimation in other control concepts

Design strategies and control methods for a thermally driven heat pump system based on combined cycles

Heating sector is one of the key emitters of greenhouse gases, and thus innovations are needed to improve the energy efficiency of heating technologies. In this paper, a recently proposed gas powered heating system that integrates an Organic Rankine Cycle (ORC) with a heat pump has been further investigated. Two different designs of the combined system were modeled, and their performances were compared and analyzed. In the first design, the cold water is firstly heated in the heat pump condenser and then further heated in the ORC condenser to achieve the required final temperature. In the second design, the water is firstly heated in the ORC condenser and then further heated in the heat pump condenser. The results showed that the first design can achieve better overall fuel-to-heat efficiency. Using Aspen Plus, a dynamic model has then been developed to study the optimal control strategies for this design when ambient conditions change. The results revealed that, for the ambient temperature range of 7–15°C, increasing air mass flow rate is sufficient to maintain the overall system performance. While when ambient temperature is below 7°C, more heat is required from the gas burner that would reduce the fuel-to-heat efficiency

Learning-Based Controller Design with Application to a Chiller Process

In this thesis, we present and study a few approaches for constructing controllers for uncertain systems, using a combination of classical control theory and modern machine learning methods. The thesis can be divided into two subtopics. The first, which is the focus of the first two papers, is dual control. The second, which is the focus of the third and last paper, is multiple-input multiple-output (MIMO) control of a chiller process. In dual control, the goal is to construct controllers for uncertain systems that in expectation minimize some cost over a certain time horizon. To achieve this, the controller must take into account the dual goals of accumulating more information about the process, by applying some probing input, and using the available information for controlling the system. This is referred to as the exploration-exploitation trade-off. Although optimal dual controllers in theory can be computed by solving a functional equation, this is usually intractable in practice, with only some simple special cases as exceptions. Therefore, it is interesting to examine methods for approximating optimal dual control. In the first paper, we take the approach of approximating the value function, which is the solution of the functional equation that can be used to deduce the optimal control, by using artificial neural networks. In the second paper, neural networks are used to represent and estimate hyperstates, which contain information about the conditional probability distributions of the system uncertainties. The optimal dual controller is a function of the hyperstate, and hence it should be useful to have a representation of this quantity when constructing an approximately optimal dual controller. The hyperstate transition model is used in combination with a reinforcement learning algorithm for constructing a dual controller from stochastic simulations of a system model that includes models of the system uncertainties. In the third paper, we suggest a simple reinforcement learning method that can be used to construct a decoupling matrix that allows MIMO control of a chiller process. Compared to the commonly used single-input single-output (SISO) structures, these controllers can decrease the variations in some system signals. This makes it possible to run the system at operating points closer to some constraints, which in turn can enable more energy-efficient operation

In-Cylinder Pressure-Based Control of Premixed Dual-Fuel Combustion

[ES] La actual crisis climática ha instado a la comunidad investigadora y a los fabricantes a brindar soluciones para hacer que el sector del transporte sea más sostenible. De entre las diversas tecnologías propuestas, la combustión a baja temperatura ha sido objeto de una extensa investigación. La combustión premezclada dual-fuel es uno de los conceptos que abordan el compromiso de NOx-hollín en motores de encendido por compresión manteniendo alta eficiencia térmica. Esta combustión hace uso de dos combustibles con diferentes reactividades para mejorar la controlabilidad de este modo de combustión en un amplio rango de funcionamiento. De manera similar a todos los modos de combustión premezclados, esta combustión es sensible a las condiciones de operación y suele estar sujeta a variabilidad cíclica con gradientes de presión significativos. En consecuencia, se requieren estrategias de control avanzadas para garantizar un funcionamiento seguro y preciso del motor. El control en bucle cerrado es una herramienta eficaz para abordar los desafíos que plantea la combustión premezclada dual-fuel. En este tipo de control, para mantener el funcionamiento deseado, las acciones de control se adaptan y corrigen a partir de una retroalimentación con las señales de salida del motor. Esta tesis presenta estrategias de control basadas en la medición de la señal de presión en el cilindro, aplicadas a motores de combustión premezclada dual-fuel. En ella se resuelven diversos aspectos del funcionamiento del motor mediante el diseño de controladores dedicados, haciéndose especial énfasis en analizar e implementar estas soluciones a los diferentes niveles de estratificación de mezcla considerados en estos motores (es decir, totalmente, altamente y parcialmente premezclada). Inicialmente, se diseñan estrategias de control basadas en el procesamiento de la señal de presión en el cilindro y se seleccionan acciones proporcionales-integrales para asegurar el rendimiento deseado del motor sin exceder las limitaciones mecánicas del motor. También se evalúa la técnica extremum seeking para realizar una supervisión de una combustión eficiente y la reducción de emisiones de NOx. Luego se analiza la resonancia de la presión en el cilindro y se implementa un controlador similar a aquel usado para el control de knock para garantizar el funcionamiento seguro del motor. Finalmente, se utilizan modelos matemáticos para diseñar un modelo orientado a control y un observador que tiene como objetivo combinar las señales medidas en el motor para mejorar las capacidades de predicción y diagnóstico en dicha configuración de motor. Los resultados de este trabajo destacan la importancia de considerar el control en bucle cerrado para abordar las limitaciones encontradas en los modos de combustión premezclada. En particular, el uso de la medición de presión en el cilindro muestra la relevancia y el potencial de esta señal para desarrollar estrategias de control complejas y precisas.[CA] L'actual crisi climàtica ha instat a la comunitat investigadora i als fabricants a brindar solucions per a fer que el sector del transport siga més sostenible. D'entre les diverses tecnologies proposades, la combustió a baixa temperatura ha sigut objecte d'una extensa investigació. La combustió premesclada dual-fuel és un dels conceptes que aborden el compromís de NOx-sutge en motors d'encesa per compressió mantenint alta eficiència tèrmica. Aquesta combustió fa ús de dos combustibles amb diferents reactivitats per a millorar la controlabilitat d'aquest tipus de combustió en un ampli rang de funcionament. De manera similar a tots els tipus de combustió premesclada, aquesta combustió és sensible a les condicions d'operació i sol estar subjecta a variabilitat cíclica amb gradients de pressió significatius. En conseqüència, es requereixen estratègies de control avançades per a garantir un funcionament segur i precís del motor. El control en bucle tancat és una eina eficaç per a abordar els desafiaments que planteja la combustió premesclada dual-fuel. En aquesta mena de control, per a mantindre el funcionament desitjat, les accions de control s'adapten i corregeixen a partir d'una retroalimentació amb els senyals d'eixida del motor. Aquesta tesi presenta estratègies de control basades en el mesurament del senyal de pressió en el cilindre, aplicades a motors de combustió premesclada dual-fuel. En ella es resolen diversos aspectes del funcionament del motor mitjançant el disseny de controladors dedicats, fent-se especial èmfasi a analitzar i implementar aquestes solucions als diferents nivells d'estratificació de mescla considerats en aquests motors (és a dir, totalment, altament i parcialment premesclada). Inicialment, es dissenyen estratègies de control basades en el processament del senyal de pressió en el cilindre i se seleccionen accions proporcionals-integrals per a assegurar el rendiment desitjat del motor sense excedir les limitacions mecàniques del motor. També s'avalua la tècnica extremum seeking per a realitzar una supervisió d'una combustió eficient i la reducció d'emissions de NOx. Després s'analitza la ressonància de la pressió en el cilindre i s'implementa un controlador similar a aquell usat per al control de knock per a garantir el funcionament segur del motor. Finalment, s'utilitzen models matemàtics per a dissenyar un model orientat a control i un observador que té com a objectiu combinar els senyals mesurats en el motor per a millorar les capacitats de predicció i diagnòstic en aquesta configuració de motor. Els resultats d'aquest treball destaquen la importància de considerar el control en bucle tancat per a abordar les limitacions trobades en la combustió premesclada. En particular, l'ús del mesurament de pressió en el cilindre mostra la rellevància i el potencial d'aquest senyal per a desenvolupar estratègies de control complexes i precises.[EN] The current climate crisis has urged the research community and manufacturers to provide solutions to make the transportation sector cleaner. Among the various technologies proposed, low temperature combustion has undergone extensive investigation. Premixed dual-fuel combustion is one of the concepts addressing the NOx-soot trade-off in compression ignited engines, while maintaining high thermal efficiency. This combustion makes use of two fuels with different reactivities in order to improve the controllability of this combustion mode over a wide range of operation. Similarly to all premixed combustion modes, this combustion is nevertheless sensitive to the operating conditions and traditionally exhibits cycle-to-cycle variability with significant pressure gradients. Consequently, advanced control strategies to ensure a safe and accurate operation of the engine are required. Feedback control is a powerful approach to address the challenges raised by the premixed dual-fuel combustion. By measuring the output signals from the engine, strategies can be developed to adapt and correct the control actions to maintain the desired operation. This thesis presents control strategies, based on the in-cylinder pressure signal measurement, applied to premixed dual-fuel combustion engines. Various objectives were addressed by designing dedicated controllers, where a special emphasis was made towards analyzing and implementing these solutions to the different levels of mixture stratification considered in these engines (i.e., fully, highly and partially premixed). At first, feedback control strategies based on the in-cylinder pressure signal processing were designed. Proportional-integral actions were selected to ensure the desired engine performance without exceeding the mechanical constraints of the engine. Extremum seeking was evaluated to track efficient combustion phasing and NOx emissions reduction. The in-cylinder pressure resonance was then analyzed and a knock-like controller was implemented to ensure safe operation of the engine. Finally, mathematical models were used to design a control-oriented model and a state observer that aimed to leverage the signals measured in the engine to improve the prediction and diagnostic capabilities in such engine configuration. The results from this work highlighted the importance of considering feedback control to address the limitations encountered in premixed combustion modes. Particularly, the use of the in-cylinder pressure measurement showed the relevance and potential of this signal to develop complex and accurate control strategies.This thesis was financially supported by the Programa Operativo del Fondo Social Europeo (FSE) de la Comunitat Valenciana 2014-2020 through grant ACIF/2018/141.Barbier, ARS. (2022). In-Cylinder Pressure-Based Control of Premixed Dual-Fuel Combustion [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/18327

Dynamic modeling and control strategies of organic Rankine cycle systems: Methods and challenges

Organic Rankine cycle systems are suitable technologies for utilization of low/medium-temperature heat sources, especially for small-scale systems. Waste heat from engines in the transportation sector, solar energy, and intermittent industrial waste heat are by nature transient heat sources, making it a challenging task to design and operate the organic Rankine cycle system safely and efficiently for these heat sources. Therefore, it is of crucial importance to investigate the dynamic behavior of the organic Rankine cycle system and develop suitable control strategies. This paper provides a comprehensive review of the previous studies in the area of dynamic modeling and control of the organic Rankine cycle system. The most common dynamic modeling approaches, typical issues during dynamic simulations, and different control strategies are discussed in detail. The most suitable dynamic modeling approaches of each component, solutions to common problems, and optimal control approaches are identified. Directions for future research are provided. The review indicates that the dynamics of the organic Rankine cycle system is mainly governed by the heat exchangers. Depending on the level of accuracy and computational effort, a moving boundary approach, a finite volume method or a two-volume simplification can be used for the modeling of the heat exchangers. From the control perspective, the model predictive controllers, especially improved model predictive controllers (e.g. the multiple model predictive control, switching model predictive control, and non-linear model predictive control approach), provide excellent control performance compared to conventional control strategies (e.g. proportional–integral controller, proportional–derivative controller, and proportional–integral–derivative controllers). We recommend that future research focuses on the integrated design and optimization, especially considering the design of the heat exchangers, the dynamic response of the system and its controllability

Fuel Cell Renewable Hybrid Power Systems

Climate change is becoming visible today, and so this book—through including innovative solutions and experimental research as well as state-of-the-art studies in challenging areas related to sustainable energy development based on hybrid energy systems that combine renewable energy systems with fuel cells—represents a useful resource for researchers in these fields. In this context, hydrogen fuel cell technology is one of the alternative solutions for the development of future clean energy systems. As this book presents the latest solutions, readers working in research areas related to the above are invited to read it