Design and optimization of heat exchangers for Organic Rankine cycles

Design of power plant and heat exchangers. Optimization with multi-objective genetic algorithm. Selection of optimal working fluid

Predictive Controller for Refrigeration Systems Aimed to Electrical Load Shifting and Energy Storage

The need to reduce greenhouse gas emissions is leading to an increase in the use of renewable energy sources. Due to the aleatory nature of these sources, to prevent grid imbalances, smart management of the entire system is required. Industrial refrigeration systems represent a source of flexibility in this context: being large electricity consumers, they can allow large-load shifting by varying separator levels or storing surplus energy in the products and thus balancing renewable electricity production. The work aims to model and control an industrial refrigeration system used for freezing food by applying the Model Predictive Control technique. The controller was developed in Matlab® and implemented in a Model-in-the-Loop environment. Two control objectives are proposed: the first aims to minimize total energy consumption, while the second also focuses on utilizing the maximum amount of renewable energy. The results show that the innovative controller allows energy savings and better exploitation of the available renewable electricity, with a 4.5% increase in its use, compared to traditional control methods. Since the proposed software solution is rapidly applicable without the need to modify the plant with additional hardware, its uptake can contribute to grid stability and renewable energy exploitation

Optimization of a multi-generation power, desalination, refrigeration and heating system

The optimization of a multi-generation system which represents the integrated dual-purpose desalination plant and a low-scale absorption refrigeration system is addressed. A nonlinear mathematical programming optimization model that integrates a natural gas combined-cycle, a multi-effect distillation desalination plant, a series flow double-effect water-lithium bromide absorption refrigeration system, and a water heater, is developed based on first-principle models. The model is implemented in General Algebraic Modelling System and a generalized gradient-based optimization algorithm is used. Given design specifications for electricity generation (around 37 MW), freshwater production (100 kg/s), refrigeration capacity (2 MW), and thermal load for heating (around 0.7 MW of hot water), the integrated system is optimized by minimizing two objective functions by single-objective optimization: total heat transfer area and total annual cost. As a result, minimum total heat transfer area values of 39148 m2, 36002 m2, and 35161 m2 are obtained when 4, 5, and 6 distillation effects were considered in the multi-effect distillation system, respectively. Also, a minimum annual cost of around 24 MM$/yr. is obtained for 5 distillation effects. The influence of the number of effects in the multi-effect distillation subsystem on the optimal solutions is analyzed. Cost-effective optimal solutions are developed for the studied multi-generation system.Fil: Pietrasanta, Ariana Milagros. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Mussati, Sergio Fabian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Aguirre, Pio Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Morosuk, Tatiana. Technishe Universitat Berlin; AlemaniaFil: Mussati, Miguel Ceferino. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentin

4E analysis of a two-stage refrigeration system through surrogate models based on response surface methods and hybrid grey wolf optimizer

Refrigeration systems are complex, non-linear, multi-modal, and multi-dimensional. However, traditional methods are based on a trial and error process to optimize these systems, and a global optimum operating point cannot be guaranteed. Therefore, this work aims to study a two-stage vapor compression refrigeration system (VCRS) through a novel and robust hybrid multi-objective grey wolf optimizer (HMOGWO) algorithm. The system is modeled using response surface methods (RSM) to investigate the impacts of design variables on the set responses. Firstly, the interaction between the system components and their cycle behavior is analyzed by building four surrogate models using RSM. The model fit statistics indicate that they are statistically significant and agree with the design data. Three conflicting scenarios in bi-objective optimization are built focusing on the overall system following the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP) decision-making methods. The optimal solutions indicate that for the first to third scenarios, the exergetic efficiency (EE) and capital expenditure (CAPEX) are optimized by 33.4% and 7.5%, and the EE and operational expenditure (OPEX) are improved by 27.4% and 19.0%. The EE and global warming potential (GWP) are also optimized by 27.2% and 19.1%, where the proposed HMOGWO outperforms the MOGWO and NSGA-II. Finally, the K-means clustering technique is applied for Pareto characterization. Based on the research outcomes, the combined RSM and HMOGWO techniques have proved an excellent solution to simulate and optimize two-stage VCRS

A HYBRID AIR CONDITIONER DRIVEN BY A HYBRID SOLAR COLLECTOR

The objective of this thesis is to search for an efficient way of utilizing solar energy in air conditioning applications. The current solar Air Conditioners (A/C)s suffer from low Coefficient of Performance (COP) and performance degradation in hot and humid climates. By investigating the possible ways of utilizing solar energy in air conditioning applications, the bottlenecks in these approaches were identified. That resulted in proposing a novel system whose subsystem synergy led to a COP higher than unity. The proposed system was found to maintain indoor comfort at a higher COP compared to the most common solar A/Cs, especially under very hot and humid climate conditions. The novelty of the proposed A/C is to use a concentrating photovoltaic/thermal collector, which outputs thermal and electrical energy simultaneously, to drive a hybrid A/C. The performance of the hybrid A/C, which consists of a desiccant wheel, an enthalpy wheel, and a vapor compression cycle (VCC), was investigated experimentally. This work also explored the use of a new type of desiccant material, which can be regenerated with a low temperature heat source. The experimental results showed that the hybrid A/C is more effective than the standalone VCC in maintaining the indoor conditions within the comfort zone. Using the experimental data, the COP of the hybrid A/C driven by a hybrid solar collector was found to be at least double that of the current solar A/Cs. The innovative integration of its subsystems allows each subsystem to do what it can do best. That leads to lower energy consumption which helps reduce the peak electrical loads on electric utilities and reduces the consumer operating cost since less energy is purchased during the on peak periods and less solar collector area is needed. In order for the proposed A/C to become a real alternative to conventional systems, its performance and total cost were optimized using the experimentally validated model. The results showed that for an electricity price of 0.12 $/kW-hr, the hybrid solar A/C's cumulative total cost will be less than that of a standard VCC after 17.5 years of operation

Adaptive Gradient Assisted Robust Optimization with Applications to LNG Plant Enhancement

About 8% of the natural gas feed to a Liquefied Natural Gas (LNG) plant is consumed for liquefaction. A significant challenge in optimizing engineering systems, including LNG plants, is the issue of uncertainty. To exemplify, each natural gas field has a different gas composition, which imposes an important uncertainty in LNG plant design. One class of optimization techniques that can handle uncertainty is robust optimization. A robust optimum is one that is both optimum and relatively insensitive to the uncertainty. For instance, a mobile LNG plant should be both energy efficient and its performance be insensitive to the natural gas composition. In this dissertation to enhance the energy efficiency of the LNG plants, first, several new options are investigated. These options involve both liquefaction cycle enhancements and driver cycle (i.e., power plant) enhancements. Two new liquefaction cycle enhancement options are proposed and studied. For enhancing the diver cycle performance, ten novel driver cycle configurations for propane pre-cooled mixed refrigerant cycles are proposed, explored and compared with five different conventional driver cycle options. Also, two novel robust optimization techniques applicable to black-box engineering problems are developed. The first method is called gradient assisted robust optimization (GARO) that has a built-in numerical verification scheme. The other method is called quasi-concave gradient assisted robust optimization (QC-GARO). QC-GARO has a built-in robustness verification that is tailored for problems with quasi-concave functions with respect to uncertain variables. The performance of GARO and QC-GARO methods is evaluated by using seventeen numerical and engineering test problems and comparing their results against three previous methods from the literature. Based on the results it was found that, compared to the previous considered methods, GARO was the only one that could solve all test problems but with a higher computational effort compared to QC-GARO. QC-GARO's computational cost was in the same order of magnitude as the fastest previous method from the literature though it was not able to solve all the test problems. Lastly the GARO robust optimization method is used to devise a refrigerant for LNG plants that is relatively insensitive to the uncertainty from natural gas mixture composition

DEVELOPMENT OF AN ADVANCED HEAT EXCHANGER MODEL FOR STEADY STATE AND FROSTING CONDITIONS

Air-to-refrigerant fin-and-tube heat exchangers are a key component in the heating, air conditioning and refrigeration industry. Considering their dominance, the industry has focused immensely on employing computer modeling in their design and development. Recently, advances in manufacturing capabilities, heat exchanger technology coupled with the move towards new environment-friendly refrigerants provide unprecedented challenges for designers and opportunities for researchers. In addition, the field of Computational Fluid Dynamics (CFD) has assumed a greater role in the design of heat exchangers. This research presents the development of an advanced heat exchanger model and design tool which aims to provide greater accuracy, design flexibility and unparalleled capabilities compared to existing heat exchanger models. The heat exchanger model developed here achieves the following. * Account for tube-to-tube conduction along fins, which is known to degrade the performance of heat exchangers, especially in carbon dioxide gas coolers * Study and develop heat exchangers with arbitrary fin sheets, which meet performance as well as packaging goals with minimal consumption of resources * Allow engineers to integrate CFD results for air flow through a heat exchanger, which the modeling tool employs to develop its air propagation sequence leading to improved accuracy over existing models which assume normal air flow propagation * Function in a quasi-steady state mode for the purpose of simulating frost accumulation and growth on heat exchangers, and completely simulate local heat transfer degradation, as well as blockage of flow passage on air side Additionally, the heat exchanger model was used to investigate gains that are enabled due to the presence of cut fins in carbon dioxide gas coolers and develop design guidelines for engineers. Finally, this dissertation analyzes the implications of minimum entropy generation on heat exchanger performance criteria of heat capacity and pressure drop, as well as evaluates the ability of entropy generation minimization as a design objective. This also serves as the first step toward an expert knowledge-based system for guiding engineers towards better designs, during the process of heat exchanger design

EXPERIMENTAL AND THEORETICAL STUDIES OF A CO-GENERATION SYSTEM FOR AN INDUSTRIAL PLANT

Ph.DDOCTOR OF PHILOSOPH

A Simple Approach to Calculate/Minimize the Refrigeration Power Requirements

The vapor compression refrigeration cycle is the most common method used for removing heat from a lower temperature level to a higher temperature level using a mechanical work. At lower temperatures (typically lower than −40°C), complex refrigeration schemes, such as cascaded refrigeration cycles, may be needed, increasing the complexity of the models used to predict the power requirements. This chapter introduces a new linear refrigeration model to predict the shaft power demand of the refrigeration cycle given the cooling demand, the condensing, and evaporation temperatures. The refrigeration model is based on regression of rigorous simulation results. This chapter also proposes a new systematic optimization method for minimizing the work consumed in refrigeration system. The methodology employs nonlinear model to find the optimum refrigeration temperature levels and their cooling duties. To solve the nonlinear problem, generalized reduced gradient algorithm is used. A case study is presented to demonstrate the advantage using of the proposed methodology. Results show that the difference in power prediction between rigorous simulation and the new refrigeration model is about 10%. The work consumed in refrigeration cycle can be reduced to 9% compared to the base case when the operating conditions are optimized