Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions

Contribution to intelligent monitoring and failure prognostics of industrial systems.

This thesis was conducted within the framework of SMART project funded by a European program, Interreg POCTEFA. The project aims to support small and medium-sized companies to increase their competitiveness in the context of Industry 4.0 by developing intelligent monitoring tools for autonomous system health management. To do so, in this work, we propose efficient data-driven algorithms for prognostics and health management of industrial systems. The first contribution consists of the construction of a new robust health indicator that allows clearly separating different fault states of a wide range of systems’ critical components. This health indicator is also efficient when considering multiples monitoring parameters under various operating conditions. Next, the second contribution addresses the challenges posed by online diagnostics of unknown fault types in dynamic systems, particularly the detection, localization, and identification of the robot axes drifts origin when these drifts have not been learned before. For this purpose, a new online diagnostics methodology based on information fusion from direct and indirect monitoring techniques is proposed. It uses the direct monitoring way to instantaneously update the indirect monitoring model and diagnose online the origin of new faults. Finally, the last contribution deals with the prognostics issue of systems failure in a controlled industrial process that can lead to negative impacts in long-term predictions. To remedy this problem, we developed a new adaptive prognostics approach based on the combination of multiple machine learning predictions in different time horizons. The proposed approach allows capturing the degradation trend in long-term while considering the state changes in short-term caused by the controller activities, which allows improving the accuracy of prognostics results. The performances of the approaches proposed in this thesis were investigated on different real case studies representing the demonstrators of the thesis partners

Aircraft system-level diagnosis with emphasis on maintenance decisions

This paper proposes a diagnostic technique that can predict component degradation for a number of complex systems. It improves and clarifies the capabilities of a previously proposed diagnostic approach, by identifying the degradation severity of the examined components, and uses a 3D Principal Component Analysis approach to provide an explanation for the observed diagnostic accuracy. The diagnostic results are then used, in a systematic way, to influence maintenance decisions. Having been developed for the Auxiliary Power Unit (APU), the flexibility and power of the diagnostic methodology is shown by applying it to a completely new system, the Environmental Control System (ECS). A major conclusion of this work is that the proposed diagnostic approach is able to correctly predict the health state of two aircraft systems, and potentially many more, even in cases where different fault combinations result in similar fault patterns. Based on the engineering simulation approach verified here, a diagnostic methodology suitable from aircraft conception to retirement is proposed

Modelling, monitoring and control of reverse somsis desalination plants using data-based techniques

This thesis presents two successful applications of data-driven models developed for a reverse osmosis desalination pilot plant. The Support Vector Regression (SVR) modeling approach for steady state operation of the reverse osmosis pilot plant M3 has performed better results than the commonly used first principle-based models. In the same way, the dynamic models have enabled the short-term prediction and simulation of the M3 plant behavior under non-steady state operation, with such a high accuracy that it makes the approach suitable for advanced reverse osmosis plant control algorithms, fault tolerant control and process optimization. In fact, SVR models have been proved to perform excellent results as part of a fault detection and isolation tool for the M3 plant. In addition, an alternative to SVR models which uses Self-Organizing Maps has been also demonstrated for fault detection and isolation, including a useful visual tool for the rapid fault detection during plant operation.Aquesta tesi presenta dues aplicacions de models basats en dades desenvolupades per a una planta pilot de dessalinització d’aigua mitjançant osmosi inversa. S’ha modelat l’operació estacionària de la planta M3 utilitzant Support Vector Regression (SVR), obtenint uns resultats millors que els dels models basats en primers principis. Així mateix, els models dinàmics han permès la simulació i la predicció a curt termini de l’M3 en condicions no estacionàries amb tal precisió que els fa idonis per a la seva aplicació en optimització de processos i algoritmes avançats de control. De fet, models SVR han estat utilitzats en un sistema de detecció i aïllament de fallades per a l’M3, obtenint excel•lents resultats. Addicionalment, també s’ha desenvolupat un sistema de detecció i aïllament de fallades alternatiu als models SVR que utilitza Self-Organizing-Maps i a més inclou una eina visual per a la ràpida detecció de fallades durant l’operació de la planta

Energy Efficient Control and Fault Detection for HVAC Systems

The interest in HVAC (Heating, Ventilation and Air-Conditioning) technology has rapidly increased in the last years. HVAC systems have become important in the design of medium-large buildings in order to ensure thermal comfort in the environments with respect to the temperature and humidity of the air. Control, optimisation and maintenance procedures are fundamental in HVAC systems in order to guarantee people comfort and energy efficient solutions in their management. Two different topics are covered in this thesis. Energy Efficient Control of Ice Thermal Energy Storage Systems HVAC plants have recently begun to be matched with thermal energy storage systems. If properly designed, installed, and maintained, these systems can be used to store energy when its cost is low and exploiting it when the price increases. In particular, in HVAC cooling systems, a common thermal storage medium is ice. From a control and optimisation point of view, a cooling plant with ice storage proves to be a complex system. Standard control strategies seem not to be able to achieve the right trade-off between energy efficiency and demand satisfaction. In this thesis, in order to design efficient control strategies for storage systems, a HVAC model with ice storage is developed in a simulation environment. The thermal behaviour of the HVAC system is derived from the mass and energy conservation equations; in particular the ice storage is considered a hybrid system, thus taking into consideration both sensible and latent heat. Three standard control methods are compared with a non-linear predictive control strategy. The simulations results show that the implemented non-linear predictive control strategy provides the best control for the efficient energy management of ice storage systems. Fault Detection in HVAC Systems Operating problems associated with degraded equipment, poor maintenance, and improperly implemented controls, plague many HVAC systems. Fault detection methods can therefore play a key role in monitoring complex HVAC plants, detecting anomalous behaviours in such a way as to keep the systems in their best operational conditions with minimum costs. In this thesis, fault detection and diagnosis methods on variable air volume (VAV) systems are first designed. To this aim, a VAV system model with two zones is developed; the control of system is obtained with a direct feedback linearisation technique. Supervised classification methods are used to detect and diagnose the simulated faults in the model. The simulations results show the good performances of the classification in the detection and diagnosis of the most common faults in VAV systems. Detection methods are then developed for the most relevant faults affecting chillers. To this aim, data collected in the research project 1043-RP promoted by ASHRAE (American Society of Heating, Refrigerating and Air Conditioning Engineers) are used. In this project experimental studies were conducted on a centrifugal water-cooled chiller in order to collect data in both normal and faulty situations. The developed technique is based on one-class classification methods with a novelty detection approach, where only normal data are used to characterize the correct system behaviour. The classification results confirm the effectiveness of the proposed method for the detection of the most common faults in chillers

Application of probabilistic deep learning models to simulate thermal power plant processes

Deep learning has gained traction in thermal engineering due to its applications to process simulations, the deeper insights it can provide and its abilities to circumvent the shortcomings of classic thermodynamic simulation approaches by capturing complex inter-dependencies. This works sets out to apply probabilistic deep learning to power plant operations using historic plant data. The first study presented, entails the development of a steady-state mixture density network (MDN) capable of predicting effective heat transfer coefficients (HTC) for the various heat exchanger components inside a utility scale boiler. Selected directly controllable input features, including the excess air ratio, steam temperatures, flow rates and pressures are used to predict the HTCs. In the second case study, an encoder-decoder mixturedensity network (MDN) is developed using recurrent neural networks (RNN) for the prediction of utility-scale air-cooled condenser (ACC) backpressure. The effects of ambient conditions and plant operating parameters, such as extraction flow rate, on ACC performance is investigated. In both case studies, hyperparameter searches are done to determine the best performing architectures for these models. Comparisons are drawn between the MDN model versus standard model architecture in both case studies. The HTC predictor model achieved 90% accuracy which equates to an average error of 4.89 W m2K across all heat exchangers. The resultant time-series ACC model achieved an average error of 3.14 kPa, which translate into a model accuracy of 82%

Machine learning solutions for maintenance of power plants

The primary goal of this work is to present analysis of current market for predictive maintenance software solutions applicable to a generic coal/gas-fired thermal power plant, as well as to present a brief discussion on the related developments of the near future. This type of solutions is in essence an advanced condition monitoring technique, that is used to continuously monitor entire plants and detect sensor reading deviations via correlative calculations. This approach allows for malfunction forecasting well in advance to a malfunction itself and any possible unforeseen consequences. Predictive maintenance software solutions employ primitive artificial intelligence in the form of machine learning (ML) algorithms to provide early detection of signal deviation. Before analyzing existing ML based solutions, structure and theory behind the processes of coal/gas driven power plants is going to be discussed to emphasize the necessity of predictive maintenance for optimal and reliable operation. Subjects to be discussed are: basic theory (thermodynamics and electrodynamics), primary machinery types, automation systems and data transmission, typical faults and condition monitoring techniques that are also often used in tandem with ML. Additionally, the basic theory on the main machine learning techniques related to malfunction prediction is going to be briefly presented