Thermoelectric system applications in buildings: A review of key factors and control methods

A low coefficient of performance (COP) limits the development of thermoelectric (TE) systems in buildings. However, considering their good integration with solar systems and budling structures, there is good application potential for TE systems in buildings. In many previous works, control systems indeed help TE systems to improve their performance. Therefore, the objective of this work is to analyze and summarize key factors in the control process and control methods for designing and optimizing the control systems for TE systems in buildings. This work reviews relevant publications from 2000 to 2022 on control applications of TE systems in different fields and groups them into key factors and control methods. The analysis of the key factors indicates the power strength of Peltier cells, the number of working Peltier cells, the temperature difference between the cold and hot sides, and the temperature difference between the object side and the indoor space as significant factors. Additionally, the most relevant control methods for the operating voltage or current are also classified. It is crucial to appropriately adjust these key factors using suitable control methods to achieve improved COP. Regarding the control application of TE systems in buildings, this is an issue that has not been studied with specific attention. Therefore, the analysis of key factors and control methods is meaningful for control systems to improve the performance of TE systems in buildings, especially under dynamic operating conditions of the built environment

Prediction of Waste Heat Energy Recovery Performance in a Naturally Aspirated Engine Using Artificial Neural Network

he waste heat from exhaust gases represents a signiicant amount of thermal energy, which has conventionally been used for combined heating and power applications. his paper explores the performance of a naturally aspirated spark ignition engine equipped with waste heat recovery mechanism (WHRM).he experimental and simulation test results suggest that the concept is thermodynamically feasible and could signiicantly enhance the system performance depending on the load applied to the engine. he simulation method is created using an artiicial neural network (ANN) which predicts the power produced from theWHRM

Prediction of Waste Heat Energy Recovery Performance in a Naturally Aspirated Engine Using Artificial Neural Network

The waste heat from exhaust gases represents a significant amount of thermal energy, which has conventionally been used for combined heating and power applications. This paper explores the performance of a naturally aspirated spark ignition engine equipped with waste heat recovery mechanism (WHRM). The experimental and simulation test results suggest that the concept is thermodynamically feasible and could significantly enhance the system performance depending on the load applied to the engine. The simulation method is created using an artificial neural network (ANN) which predicts the power produced from theWHRM

Double smart energy harvesting system for self-powered industrial IoT

312 p. 335 p. (confidencial)Future factories would be based on the Industry 4.0 paradigm. IndustrialInternet of Things (IIoT) represent a part of the solution in this field. Asautonomous systems, powering challenges could be solved using energy harvestingtechnology. The present thesis work combines two alternatives of energy input andmanagement on a single architecture. A mini-reactor and an indoor photovoltaiccell as energy harvesters and a double power manager with AC/DC and DC/DCconverters controlled by a low power single controller. Furthermore, theaforementioned energy management is improved with artificial intelligencetechniques, which allows a smart and optimal energy management. Besides, theharvested energy is going to be stored in a low power supercapacitor. The workconcludes with the integration of these solutions making IIoT self-powered devices.IK4 Teknike

Prediction and analytics of operating parameters on thermoelectric generator energy generation

PhD ThesisThe efficient use of energy at all stages along the energy supply chain and the utilization of renewable energies are very important elements of a sustainable energy supply system, specially at the conversion from thermal to electrical energy. Converting the low-grade waste heat into electrical power would be useful and effective for several primary and secondary applications. One of the viable means to convert the low-grade waste heat into electrical energy is the use of thermoelectric power conversion. The performance of thermoelectric generators, subjected to thermal effects, can vary considerably depending on the operating conditions, therefore it is necessary to measure and have a better understanding of the characteristics and performance of the thermoelectric generator. It is important to understand the thermoelectric generator’s dynamic behavior and interaction with its operating environmental parameters. Based on this knowledge, it is then significant to develop an effective mathematical model that can provide the user with the most probable outcome of the output voltage. This will contribute to its reliability and calculation to increase the overall efficiency of the system. This thesis provides the transient solution to the three-dimensional heat transfer equation with internal heat generation. It goes on to describes the transfer and generation of heat across the thermoelectric generator with dynamic exchange of heat. This solution is then included in a model in which the thermal masses and the operating environmental parameters of the thermoelectric generator are factored in. The resulting model is created in MATLAB. The comparison with experimental results from a thermoelectric generator system confirms the accuracy of the artificial neural network model. This thesis also presents two practical applications, the prediction of the input parameters with a given output voltage, and sensitivity analysis designed for the model. This is to enable users to customize the thermoelectric generator for their requirements. This allows for better usage of resources eventually

DESIGN OF A NOVEL THERMO-ELECTRIC COOLING DEVICE CAPABLE OF ACHIEVING CRYOGENIC TEMPERATURES FOR DENTAL PULP TESTING

Dental pulp testing is a diagnostic test in endodontics to test whether the dental pulp is dead or alive. Thermal tests (cold and hot) and electrical pulp testing techniques are two of the most common pulp sensibility tests currently being used. Although cold tests have shown more promising results in comparison to other techniques, the current methods used for cold testing have safety concerns as they involve direct application of the cold agent to the tooth. This study proposed a thermoelectric cooling based dental pulp testing device capable of achieving cryogenic temperatures and varying this temperature below 0℃ up to -60℃. This device is safe in operation and provides availability for on-site application due to its portability and stand-alone features. Thermoelectric cooling is based on the Peltier effect, which allows a temperature difference across a thermoelectric module and results in one side of the module becoming cold while the other side becomes hot. The challenge for such devices based on the Peltier effect is that the heat on the hot side of the module needs to be dissipated so that it is not too hot to burn the patient’s skin. This study explored the application of the phase change cooling technique in the form of heat pipes and vapor chambers to address this challenge. Finally, a thermoelectric cooling system capable of achieving -60℃ at the probe for pulp sensibility testing was proposed through modeling and simulation in Comsol Multiphysics software and experimentally validated using off-the-shelf hardware

Advanced control and optimisation of DC-DC converters with application to low carbon technologies

Prompted by a desire to minimise losses between power sources and loads, the aim of this Thesis is to develop novel maximum power point tracking (MPPT) algorithms to allow for efficient power conversion within low carbon technologies. Such technologies include: thermoelectric generators (TEG), photovoltaic (PV) systems, fuel cells (FC) systems, wind turbines etc. MPPT can be efficiently achieved using extremum seeking control (ESC) also known as perturbation based extremum seeking control. The basic idea of an ESC is to search for an extrema in a closed loop fashion requiring only a minimum of a priori knowledge of the plant or system or a cost function. In recognition of problems that accompany ESC, such as limit cycles, convergence speed, and inability to search for global maximum in the presence local maxima this Thesis proposes novel schemes based on extensions of ESC. The first proposed scheme is a variance based switching extremum seeking control (VBS-ESC), which reduces the amplitude of the limit cycle oscillations. The second scheme proposed is a state dependent parameter extremum seeking control (SDP-ESC), which allows the exponential decay of the perturbation signal. Both the VBS-ESC and the SDP-ESC are universal adaptive control schemes that can be applied in the aforementioned systems. Both are suitable for local maxima search. The global maximum search scheme proposed in this Thesis is based on extensions of the SDP-ESC. Convergence to the global maximum is achieved by the use of a searching window mechanism which is capable of scanning all available maxima within operating range. The ability of the proposed scheme to converge to the global maximum is demonstrated through various examples. Through both simulation and experimental studies the benefit of the SDP-ESC has been consistently demonstrated

NASA SBIR abstracts of 1990 phase 1 projects

The research objectives of the 280 projects placed under contract in the National Aeronautics and Space Administration (NASA) 1990 Small Business Innovation Research (SBIR) Phase 1 program are described. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses in response to NASA's 1990 SBIR Phase 1 Program Solicitation. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 280, in order of its appearance in the body of the report. The document also includes Appendixes to provide additional information about the SBIR program and permit cross-reference in the 1990 Phase 1 projects by company name, location by state, principal investigator, NASA field center responsible for management of each project, and NASA contract number

Predicting Thermoelectric Power Plants Diesel/Heavy Fuel Oil Engine Fuel Consumption Using Univariate Forecasting and XGBoost Machine Learning Models

Monitoring and controlling thermoelectric power plants (TPPs) operational parameters have become essential to ensure system reliability, especially in emergencies. Due to system complexity, operating parameters control is often performed based on technical know-how and simplified analytical models that can result in limited observations. An alternative to this task is using time series forecasting methods that seek to generalize system characteristics based on past information. However, the analysis of these techniques on large diesel/HFO engines used in Brazilian power plants under the dispatch regime has not yet been well-explored. Therefore, given the complex characteristics of engine fuel consumption during power generation, this work aimed to investigate patterns generalization abilities when linear and nonlinear univariate forecasting models are used on a representative database related to an engine-driven generator used in a TPP located in Pernambuco, Brazil. Fuel consumption predictions based on artificial neural networks were directly compared to XGBoost regressor adaptation to perform this task as an alternative with lower computational cost. AR and ARIMA linear models were applied as a benchmark, and the PSO optimizer was used as an alternative during model adjustment. In summary, it was possible to observe that AR and ARIMA-PSO had similar performances in operations and lower error distributions during full-load power output with normal error frequency distribution of −0.03 ± 3.55 and 0.03 ± 3.78 kg/h, respectively. Despite their similarities, ARIMA-PSO achieved better adherence in capturing load adjustment periods. On the other hand, the nonlinear approaches NAR and XGBoost showed significantly better performance, achieving mean absolute error reductions of 42.37% and 30.30%, respectively, when compared with the best linear model. XGBoost modeling was 8.7 times computationally faster than NAR during training. The nonlinear models were better at capturing disturbances related to fuel consumption ramp, shut-down, and sudden fluctuations steps, despite being inferior in forecasting at full-load, especially XGBoost due to its high sensitivity with slight fuel consumption variations