Machine Learning for the prediction of the dynamic behavior of a small scale ORC system

This work was funded by the 2016 Scholarship of the Knowledge Center on OrganicRankine Cycle (KC ORC, www.kcorc.org), awarded to Lorenzo Tocci to work on this researchproject with Dr Pesyridis at Brunel University London. Entropea Labs is also acknowledgedfor the economic and technical support provided during the completion of this study.2016 Scholarship of the Knowledge Center on Organic Rankine Cycle (KC ORC, www.kcorc.org

Modelling and control of waste heat recovery systems for heavy-duty applications

Internal combustion engines (ICEs) are likely to be used in heavy-duty applications for many years and it is important to continue improving their efficiency. Undesirable emissions in internal combustion engines are of major concern due to their negative effect on the human health and global warming. One approach is to recover waste heat from the exhaust of heavy-duty diesel engines (HDDEs) using waste heat recovery (WHR) technologies. WHR based on organic Rankine cycle (ORC) is a promising technology, which offers potential to reduce the fuel consumption of HDDEs by converting the wasted thermal energy to alternative useful electrical or mechanical energy. In the ORC, the evaporator is considered the most critical component of the system. Careful modelling of the evaporator unit is both crucial to assess the dynamic performance of the ORC system and challenging due to the high nonlinearity of its governing equations. This study uses an Adaptive Network-based Fuzzy Inference System (ANFIS) modelling technique to provide efficient control-oriented evaporator models for prediction of heat source and refrigerant temperatures at the evaporator outlet. The ANFIS model benefits from feed-forward output calculation and backpropagation capability of neural network, while keeping the interpretability of fuzzy systems. The effect of training the models using hybrid gradient-descent least-square estimate (GD-LSE) and particle swarm optimisation (PSO) techniques is investigated and the performance of both techniques are compared in terms of RMSE and correlation coefficients. The simulation results indicate strong learning ability and high generalisation performance for both techniques beyond capability of numerical models. However, a better accuracy is achieved for the models trained using the PSO algorithm. Experimentally-measured data is collected from a 1-kWe ORC prototype developed in Clean Energy Processes (CEP) laboratory at Imperial College London and the proposed ANFIS techniques is applied in order to investigate the application of the neuro-fuzzy technique for modelling the evaporator unit. Comparison of the experimental data and the neuro-fuzzy models predictions reveals an acceptable accuracy in predicting the evaporator outlet temperature and pressure. A novel control approach is also proposed to ensure the safe operation of ORC waste heat recovery system and stabilize its work output when subjected to transient heat sources in a range of waste heat from heavy-duty diesel engines. The control strategy comprises a neuro-fuzzy controller based on the inverse dynamics of the ORC system to control the superheating at the evaporator outlet by adjusting the pump speed and a PI controller to maintain the expander work output by regulating the mass flow rate at the expander inlet. The performance of the control strategy is investigated with respect to set-point tracking and its robustness is tested in the presence of noise. The simulation results indicate an enhancement in the controller performance by combination of feedforward and feedback controllers based on neuro-fuzzy techniques. The proposed control scheme not only can obtain satisfactory transient response under various loading conditions, but also can achieve desirable disturbance rejection performance

Surrogate Optimization Model for an Integrated Regenerative Methanol Transcritical Cycle

In order to reduce the cost ($) per megawatts hour (MWh) of electrical energy generated by a nuclear power cycle with a novel small modular reactor (SMR), a new SMR-based nuclear power cycle with Methanol as working fluid was designed. It was built virtually with the Python & Coolprop software based on all components’ physical properties, and it is therefore called the physics-based model. The physics-based model would require seven user-defined values as input for the seven free design parameters, respectively. The physics-based model outcomes include LCOE (the cost per megawatts hour of electrical energy generated by the cycle), the first-law efficiency of the power cycle (the ratio between the net power out of the power cycle and the net thermal energy into the power cycle), and the penalty (severity of system violation with the given design parameters values). In order to compare between different power cycles, the corresponding optimized LCOE are needed. However, in order to find the design parameters that optimize the system LCOE, it can take up to three days with the physics-based model, because the physics-based model is highly complex and it takes thousands of iterations averagely to the optimize the power system. In confronting the time complexity issue in optimization, the study in this paper explores the viability of replacing the physics-based model with a machine learning-based surrogate model. During the optimization procedure, the machine learning-based surrogate model is expected to accelerate process of finding the corresponding outcomes, and thus to save time. Candidate surrogate models are built and analyzed in terms of their prediction accuracy. The last chosen model is further optimized in terms of its structure and hyper-parameters. With the structurally and parametric optimized surrogate model being incorporated, different global optimizers are used and analyzed. As the result, the optimized design parameters from the surrogate-optimizer model are fed into the physics-based model, and their corresponding results are compared with the baseline optimized results of the physics-based model. In conclusion, the study reveals that the Multilayer Perceptron (MLP) networks with two hidden layers gives the best prediction performance, and therefore they are chosen as the surrogate model. In addition, four global optimizers, namely the basinhopping, the differential evolution, the dual annealing and the fmin, are working well along with the chosen surrogate model. They integrated surrogate-optimizer model is capable of finding the optimized LCOE as well as the corresponding design parameters. In comparison with the baseline optimized LCOE, the relative error is less than 3.5%, and this searching procedure completed within 30 minutes

Dynamic thermal model development of direct methanol fuel cell

Direct methanol fuel cell (DMFC) is fueled with liquid methanol coupled with air to produce power at reasonably lower operational conditions while resulting in by-products of carbon dioxide and water, which is more environmentally friendly. Due to the complexity associated with the performance of direct methanol fuel cell, the application of artificial neural network (ANN) can significantly predict the characteristic performance of the cells. Nevertheless, limited studies have delved into the exploration of artificial neural network in the prediction of the transient characteristics of direct methanol fuel cells. The current study however presents a detailed investigation into the prediction of the dynamic thermal characteristics of a direct methanol fuel cell stack subjected to varying operational environment. Parameters considered in the study as input include methanol concentration, anode as well as cathode inlet flow rates, coupled with current. Outcomes for the artificial neural network models for three varying learning algorithms were ascertained for anode and cathode temperatures, which were forecasted closely by models with higher number of hidden neurons. Such models have coefficients of determination of 0.95 or more and mean square error less than 0.04. Thus, the outcome of the study presents prospects for artificial neural network methods as optimum control approach in direct methanol fuel cell development

OPTIMUM DESIGN AND OPERATION OF COMBINED COOLING HEATING AND POWER SYSTEM WITH UNCERTAINTY

Combined cooling, heating, and power (CCHP) systems utilize renewable energy sources, waste heat energy, and thermally driven cooling technology to simultaneously provide energy in three forms. They are reliable by virtue of main grid independence and ultra-efficient because of cascade energy utilization. These merits make CCHP systems potential candidates as energy suppliers for commercial buildings. Due to the complexity of CCHP systems and environmental uncertainty, conventional design and operation strategies that depend on expertise or experience might lose effectiveness and protract the prototyping process. Automation-oriented approaches, including machine learning and optimization, can be utilized at both design and operation stages to accelerate decision-making without losing energy efficiency for CCHP systems. As the premise of design and operation for the combined system, information about building energy consumption should be determined initially. Therefore, this thesis first constructs deep learning (DL) models to forecast energy demands for a large-scale dataset. The building types and multiple energy demands are embedded in the DL model for the first time to make it versatile for prediction. The long short-term memory (LSTM) model forecasts 50.7% of the tasks with a coefficient of variation of root mean square error (CVRMSE) lower than 20%. Moreover, 60% of the tasks predicted by LSTM satisfy ASHRAE Guideline 14 with a CVRMSE under 30%. Thermal conversion systems, including power generation subsystems and waste heat recovery units, play a vital role in the overall performance of CCHP systems. Whereas a wide choice of components, nonlinear characteristics of these components challenge the automation process of system design. Therefore, this thesis second designs a configuration optimization framework consisting of thermodynamic cycle representation, evaluation, and optimizer to accelerate the system design process and maximize thermal efficiency. The framework is the first one to implement graphic knowledge and thermodynamic laws to generate new CO2 power generation (S-CO2) system configurations. The framework is then validated by optimizing the S-CO2 system's configurations under simple and complex component number limitations. The optimized S-CO2 system reaches 49.8% thermal efficiency. This efficiency is 2.3% higher than the state of the art. Third, operation strategy with uncertainty for CCHP systems is proposed in this thesis for a hospital with a floor area of 22,422 m2 at College Park, Maryland. The hospital energy demands are forecasted from the DL model. And the S-CO2 power subsystem is implemented in CCHP after optimizing from the configuration optimizer. A stochastic approximation is combined with an autoregression model to extract uncertain energy demands for the hospital. Load-following strategies, stochastic dynamic programming (SDP), and approximation approaches are implemented for CCHP system operation without and with uncertainties. As a case study, the optimization-based operation overperforms the best load-following strategy by 14% of the annual cost. Approximation-based operation strategy highly improves the computational efficiency of SDP. The daily operating cost with uncertain cooling, heating, and electricity demands is about 0.061 $/m2, and a potential annual cost is about 22.33$/m2. This thesis fills the gap in multiple energy types forecast for multiple building types via DL models, prompts the design automation of S-CO2 systems by configuration optimization, and accelerates operation optimization of a CCHP system with uncertainty by an approximation approach. In-depth data-driven methods and diversified optimization techniques should be investigated further to boost the system efficiency and advance the automation process of the CCHP system

Machine Learning Approach for Modeling and Control of a Commercial Heliocentris FC50 PEM Fuel Cell System

In recent years, machine learning (ML) has received growing attention and it has been used in a wide range of applications. However, the ML application in renewable energies systems such as fuel cells is still limited. In this paper, a prognostic framework based on artificial neural network (ANN) is designed to predict the performance of proton exchange membrane (PEM) fuel cell system, aiming to investigate the effect of temperature and humidity on the stack characteristics and on tracking control improvements. A large part of the experimental database for various operating conditions has been used in the training operation to achieve an accurate model. Extensive tests with various ANN parameters such as number of neurons, number of hidden layers, selection of training dataset, etc., are performed to obtain the best fit in terms of prediction accuracy. The effect of temperature and humidity based on the predicted model are investigated and compared to the ones obtained from real-time experiments. The control design based on the predicted model is performed to keep the stack operating point at an adequate power stage with high-performance tracking. Experimental results have demonstrated the effectiveness of the proposed model for performance improvements of PEM fuel cell system.This research was funded by the Basque Government, Diputación Foral de Álava and UPV/EHU, respectively, through the projects EKOHEGAZ (ELKARTEK KK-2021/00092), CONAVANTER and GIU20/063

In-cylinder pressure resonance analysis for trapped mass estimation in automotive engines

This thesis presents a new application for in-cylinder pressure sensors in internal combustion engines. The new method takes profit of the high-frequency content of the in-cylinder pressure signal to determine the speed of sound evolution during the expansion stroke and combines this estimation with the low-frequency content of the pressure signal and a volume estimation to obtain a measurement of the trapped mass. The new method is based on the studies of the resonance phenomenon in pent-roof combustion chambers and proposes three calibration procedures to determine the resonant frequency evolution when bowl-in-piston geometries are considered. The Fourier transform has been modified in order to include harmonics with frequency variations, which allows a rapid identification of the resonant modes with no need of time-frequency analysis, e.g. STFT or WD. The main limitation of the method resides in the resonance excitation, which may be insufficient in low-load conditions, such as idle. An observer is presented to overcome that problem. The observer takes into account the dynamics of the sensors, the dynamics at the intake manifold, and combines current flow sensors with intermittent measurements, such as the trapped mass obtained by the resonance method, to provide the system with accurate and robust measurements of the trapped mass, the EGR, and the composition at the exhaust. The trapped mass obtained by the resonance method has been compared with auxiliary methods in various experimental facilities: in a SI engine, where no EGR exist, the differences founded were below 1%, in a conventional CI light-duty engine the average of the differences over 808 operating conditions accounted for a 2.64 %, in a research heavy-duty RCCI engine, with EGR, port fuel gasoline, and direct diesel injections, the average difference was 2.17 %, and in a research two-strokes single cylinder engine, where significant short-circuit and residual gases exist, the differences founded were 4.36 %. In all the studied cases the differences founded with the reference estimation can be attributed to the auxiliary method employed and its expected error. In order to demonstrate the potential of the resonance method four applications for control and diagnosis of internal combustion engines have been proposed: the estimation of residuals in engines with NVO, the prediction of knock in SI engines, the estimation of the exhaust gases temperature, and a NOx model for CI engines. In the four applications the method was compared with current methodologies and with additional sensors, demonstrating the improvement in accuracy and a cycle-to-cycle resolution.Esta tesis presenta una nueva aplicación para los sensores de presión en cámara. El nuevo método utiliza el contenido de alta frecuencia de la señal de presión en cámara para estimar la evolución de la velocidad del sonido durante la expansión de los gases de escape y combina esta estimación con el contenido de baja frecuencia de la presión en cámara y el volumen instantáneo de la cámara para obtener una medida de la masa atrapada. El nuevo método está basado en los estudios de la resonancia en cámaras de combustión cilíndricas y propone tres procedimientos de calibración para estimar la evolución de la frecuencia de resonancia en cámaras de combustión con bowl. La transformada de Fourier ha sido modificada para considerar harmónicos con frecuencias que varían en el tiempo, lo que permite una rápida identificación de los modos de resonancia sin necesidad de utilizar un análisis en tiempo frecuencia, como por ejemplo STFT o WD. La principal limitación del método es la necesidad de excitación suficiente de la resonancia, que puede impedir su uso en condiciones de baja carga como el ralentí. Para solventar este problema se ha diseñado un observador. El observador incluye las dinámicas de los sensores, las dinámicas del colector de admisión, y combina los sensores actuales de flujo con medidas intermitentes (como la medida ofrecida por el nuevo método de la resonancia) para obtener medidas de la masa atrapada, del EGR y de la composición en el escape precisas y robustas. La medida de la masa atrapada obtenida por el método de la resonancia ha sido comparado con métodos auxiliares en diferentes instalaciones experimentales: en un motor SI, sin EGR, las diferencias con los sensores eran menores del 1%, en un motor convencional CI la media de las diferencias sobre 808 puntos de operación distintos ha sido de 2.64 %, en un motor de investigación con EGR, con inyección gasolina en el colector e inyección directa de diesel, las diferencias fueron de 2.17 %, y en un motor de investigación de dos tiempos, donde existían grandes cantidades de corto-circuito y gases residuales, las diferencias fueron de 4.36 %. En todos los casos estudiados las diferencias encontradas pueden ser atribuidas a los errores que caracterizan los métodos auxiliares utilizados para obtener la medida de referencia. Finalmente, para demostrar el potencial del método se han desarrollado cuatro aplicaciones para control y diagnóstico de motores de combustión interna alternativos: la estimación de gases residuales en motores con NVO, la predicción de knock en motores SI, la estimación de la temperatura de los gases de escape, y un modelo de NOx para motores CI. En las cuatro aplicaciones el método ha sido comparado con los sistemas de medidas actuales y con sensores adicionales, demostrando mejoras importantes en la precisión de la medida y una resolución de un solo ciclo.Aquesta tesi presenta una nova aplicació per als sensors de pressió en cambra. El nou mètode utilitza el contingut d'alta freqüència del senyal de pressió en cambra per estimar l'evolució de la velocitat del so durant l'expansió dels gasos d'eixida i combina aquesta estimació amb el contingut de baixa freqüència de la pressió en cambra i el volum instantani de la cambra per obtenir una mesura de la massa atrapada. El nou mètode està desenvolupat dels estudis de la ressonància en cambres de combustió cilíndriques i proposa tres procediments de calibratge per estimar l'evolució de la freqüència de ressonància en cambres de combustió amb bowl. La transformada de Fourier ha sigut modificada per considerar harmònics amb freqüències que varien en el temps, el que permet una ràpida identificació dels modes de ressonància sense necessitat d'utilitzar una anàlisi en temps-freqüència, com per exemple la STFT o la WD. La principal limitació del mètode és la necessitat d'excitació suficient de la ressonància, que pot impedir el seu ús en condicions de baixa càrrega, com al ralentí. Per solucionar aquest problema s'ha desenvolupat un observador. L'observador inclou les dinàmiques dels sensors, les dinàmiques del col·lector d'admissió, i combina els sensors actuals de flux amb mesures intermitents (com l'obtinguda pel nou mètode de la ressonància) per obtenir mesures de la massa atrapada, del EGR i de la composició d'eixida precises i robustes. La mesura de la massa atrapada obtinguda pel mètode de la ressonància ha sigut comparada en mètodes auxiliars en diferents instal·lacions experimentals: a un motor SI, sense EGR, les diferencies amb els sensors estaven per davall de l'1 %, a un motor convencional CI la mitja de les diferències sobre 808 punts d'operació diferents ha sigut de 2.64 %, a un motor d'investigació, en EGR, en injecció gasolina en el col·lector i injecció directa de dièsel, les diferències van ser de 2.17 %, i a un motor d'investigació de dos temps, on existien grans quantitats de curtcircuit i residuals, les diferencies foren de 4.36 %. En tots els casos estudiats les diferències trobades poden ser atribuïdes als errors que caracteritzen els mètodes auxiliars utilitzats per obtenir la mesura de referència. Finalment, per demostrar el potencial del mètode s'han desenvolupat quatre aplicacions per al control i diagnòstic de motors de combustió interna alternatius: l'estimació de gasos residuals en motors amb NVO, la predicció de knock en motors SI, l'estimació de la temperatura dels gasos d'eixida, i un model de NOx per a motors CI. En les quatre aplicacions el mètode ha sigut comparat amb els sistemes de mesures actuals i amb sensors addicionals, demostrant millores importants en la precisió de la mesura i una resolució de solament un cicle.Bares Moreno, P. (2017). In-cylinder pressure resonance analysis for trapped mass estimation in automotive engines [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/9042

Experimental investigation and modelling of the heating value and elemental composition of biomass through artificial intelligence

Abstract: Knowledge advancement in artificial intelligence and blockchain technologies provides new potential predictive reliability for biomass energy value chain. However, for the prediction approach against experimental methodology, the prediction accuracy is expected to be high in order to develop a high fidelity and robust software which can serve as a tool in the decision making process. The global standards related to classification methods and energetic properties of biomass are still evolving given different observation and results which have been reported in the literature. Apart from these, there is a need for a holistic understanding of the effect of particle sizes and geospatial factors on the physicochemical properties of biomass to increase the uptake of bioenergy. Therefore, this research carried out an experimental investigation of some selected bioresources and also develops high-fidelity models built on artificial intelligence capability to accurately classify the biomass feedstocks, predict the main elemental composition (Carbon, Hydrogen, and Oxygen) on dry basis and the Heating value in (MJ/kg) of biomass...Ph.D. (Mechanical Engineering Science