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This paper proposes a scalable and widely replicable framework for the energy management of district systems. The framework is built on a graph-based energy characterization of the system, whose supply and demand dynamics are numerically represented by hybrid mathematical models involving the available control inputs and measurable outputs. Following the graph topology connecting supply and demand nodes, a suitable optimization problem is formulated to take into consideration the actual availability of the involved control input and measurements, resulting in a computationally tractable mixed-integer linear control problem which can be implemented in a receding-horizon fashion. As an illustrative case, the framework is applied to a real-case district heating system, and the validity of the approach is showed through model-in-the-loop and system-in-the-loop numerical results. • Wide replicability and applicability of District Energy Management is a challenge. • Data-driven hybrid systems offer flexibility to capture complex system dynamics. • Replicable optimization framework based on district topological graph. • Topological graph and hybrid models captures the district energy characterization. • Performances and applicability proved in real and simulated district system. [ABSTRACT FROM AUTHOR]

Air-to-water heat pumps are one of the most common and energy efficient heating systems for buildings, particularly floor-heating plants. One way to further improve their effectiveness is to control the heat pump exploiting the dependence of its coefficient of performance (COP) on the external temperature and temperature of the return water from the load. In particular, it is possible to exploit the heat pump when its efficiency is higher, so optimizing its performance in a predictive manner, anticipating the impact of external conditions. For the case of an air-to-water heat pump, the optimization problem is nonlinear due to the load dependence of the heat pump COP and variable supply water flow rate. This may pose implementation problems. If we address a standard control hardware, simplified optimal control formulations are more effective. In this paper, we specifically address this issue, and a reduced-order, linear, but adaptive time-varying predictive model of the heat pump COP is designed. Our solution takes into account the variation of the heat pump efficiency based on the external temperature and the load profile, which are changing within the control horizon. The proposed COP model is then used within a linear time-varying model predictive controller formulation which provides a prediction of the heat pump dynamical behavior based on the load dependence of the heat pump COP, while tackling the nonlinearities of the system imposed by the variable water flow rate in the hot water tank and also by the load dependence of the heat pump COP. The proposed approach has been implemented and in detail tested on a reference model based on a real case study from the Denmark Technical University, Risø Campus, SYSLAB. An intensive simulation analysis and complements the testing, showing the accuracy and the potential of the method, also in the perspective of practical implementation. • Optimization of heat pump behavior considering variable COP. • On-line estimation of simplified COP models with EKF. • Experimental validation of the model used. • Feasibility of realtime optimization proved. [ABSTRACT FROM AUTHOR]

This paper presents an approach to design robust fixed structure controllers for uncertain systems using a finite set of measurements in the frequency domain. In traditional control system design, usually, based on measurements, a model of the plant, which is only an approximation of the physical system, is first built, and then control approaches are used to design a controller based on the identified model. Errors associated with the identification process as well as the inevitable uncertainties associated with plant parameter variations, external disturbances, measurement noise, etc. are expected to all contribute to the degradation of the performance of such a scheme. In this paper, we propose a nonparametric method that uses frequency-domain data to directly design a robust controller, for a class of uncertainties, without the need for model identification. The proposed technique, which is based on interval analysis, allows us to take into account the plant uncertainties during the controller synthesis itself. The technique relies on computing the controller parameters for which the set of all possible frequency responses of the closed-loop system are included in the envelope of a desired frequency response. Such an inclusion problem can be solved using interval techniques. The main advantages of the proposed approach are: (1) the control design does not require any mathematical model, (2) the controller is robust with respect to plant uncertainties, and (3) the controller structure can be chosen a priori , which allows us to select low-order controllers. To illustrate the proposed method and demonstrate its efficacy, an application to an air flow heating system is presented. [ABSTRACT FROM AUTHOR]

• The algorithm of recursive modified partial least square (RMPLS) is proposed and employed to enhance the precision of prediction with low computation complexity. • The problem of signal prediction is equivalently transformed into parameter prediction with the help of locally weighted projection regression (LWPR), which makes the nonlinear quadratic problem of predictive controller for nonlinear process solvable. • The proposed predictive control strategy is updated by learning from the online measurable data. It is therefore suitable for the tracking control of chemical industry with complex and changeable work conditions. In this paper, a data-driven predictive control strategy for nonlinear system is proposed and testified on a continuous stirred tank heater (CSTH) benchmark. A recursive modified partial least square (RMPLS) algorithm is employed to regress the local linear model. The algorithm of locally weighted projection regression (LWPR) is then leveraged to build the predictive model, based on which a novel data-driven predictive control strategy is put forward. The proposed predictive controller has the ability to deal with changing working conditions, benefiting from the incremental learning ability of RMPLS and LWPR. The performance of the proposed control strategy is demonstrated with the CSTH while the superiority is illustrated by comparison with an existing model-free adaptive control approach. [ABSTRACT FROM AUTHOR]

Highlights • A robust dynamic integral sliding mode control (DISMC) is designed for the approximated model to track the desired heating value. • The unknown states required for the model-based control are reconstructed using a gain-scheduled modified Utkin observer (GSMUO). • To guarantee the stability of the overall system, the boundedness of the internal dynamics is also proved. • To make a fair comparison the performance of the proposed controller is compared with an integral sliding mode and a classical PI controllers. Abstract In this paper, a model-based control and state reconstruction of an underground coal gasification (UCG) process is elaborated. In order to deploy model-based control strategies, a sophisticated model of the UCG process based on partial differential equations is approximated with a nonlinear control-oriented model that adequately preserves the fundamental dynamic characteristics of the process. A robust dynamic integral sliding mode control (DISMC) is designed based on the control-oriented model to track the desired heating value, which is one of the key indicators for evaluating the performance of an UCG process. Unknown states required for the model-based control are reconstructed using a gain-scheduled modified Utkin observer (GSMUO). In order to assess the robustness of the nonlinear control and estimation techniques, the water influx phenomenon is considered as an input disturbance. Moreover, the underlying UCG plant model is subjected to parametric variations as well as measurement noise. In order to guarantee the stability of the overall system, the boundedness of the internal dynamics is also proved. To make a fair comparison, the performance of the proposed controller is compared with an integral sliding mode control (ISMC) and a classical proportional-integral (PI) controller. Simulation results highlight the effectiveness of the proposed control scheme in terms of minimum control energy and improved tracking error. Moreover, the simulation study shows that the combination of DISMC and GSMUO exhibit robustness against an input disturbance, parametric uncertainties and measurement noise. [ABSTRACT FROM AUTHOR]

Recent research has emphasized the successful application of canonical correlation analysis (CCA) to perform fault detection (FD) in both static and dynamic processes with additive faults. However, dealing with multiplicative faults has not been as successful. Thus, this paper considers the application of CCA to deal with the detection of incipient multiplicative faults in industrial processes. The new approaches incorporate the CCA-based FD with the statistical local approach. It is shown that the methods are effective in detecting incipient multiplicative faults. Experiments using a continuous stirred tank heater and simulations on the Tennessee Eastman process are provided to validate the proposed methods. [ABSTRACT FROM AUTHOR]

This paper discusses the optimization and control of the one dimensional (1-D) packed bed model of underground coal gasification (UCG) process for an actual UCG site. The optimization is performed to compensate for the uncertainty in coal and char ultimate analysis (caused by repeated measurements of different samples) and in steam to oxygen (O 2 ) ratio at the reaction front. The constrained nonlinear optimization problem is solved by using state of the art sequential quadratic programing (SQP) algorithm to minimize the error between experimental and simulated heating values. The results of the optimized model are validated with actual filed trials which show a good match for heating value of the product gases. The super twisting controller, a second order sliding mode control (SOSMC) algorithm is successfully implemented on the process model, which keeps the heating value of the product gas at the desired level in the presence of the matched disturbance: steam produced from water influx from surrounding aquifers and due to the moisture content of the coal. [ABSTRACT FROM AUTHOR]