Process control should be one of the most exciting chemical engineering undergraduate courses! This presentation describes our experience transforming "Chemical Process Control" into "Data Analytics, Optimization, and Control" at the University of Notre Dame (required in the second semester of the junior year). In six hands-on experiments, students practice data-centric modeling and analysis using the Ardunio-based Temperature Control Lab (TCLab) hardware. The semester learning goals are:
- Develop mathematical models for dynamical systems from data and first principles using modern statistical methods;
- Predict dynamical system performance using numerical methods;
- Analyze, implement, tune, and debug feedback controllers using the hands-on laboratory;
- Formulate and solve optimization problems for decision-making;
- Demonstrate mastery of at least two of the above skills in an open-ended group project.

The goal of this presentation is to share our strategy to modernize... [more]

The growing electrification of buildings and vehicles, while a natural step towards achieving global decarbonization, poses some challenges for the electric grid in terms of power consumption. One way of addressing them is by deploying onsite, behind-the-meter resources (BTMR), such as battery energy storage and solar PV generation. The optimal design of these systems, however, is a demanding task that depends on the integration of multiple complex subsystems. In this work, the optimal integrated design and dispatch of BTMR systems for retail buildings with electric vehicle fast charging stations is addressed. A framework is proposed, combining high-fidelity simulation (of buildings, electric vehicle fast charging stations, and BTMR), predictive control strategies with closed-loop implementation, and a derivative-free design method that explores parallelization and high-performance computing. Focus is given to the design layer, highlighting the effect of parallelization on the choice o... [more]

The integrated design and control (IDC) framework is becoming increasingly important for systematic design of flexible manufacturing and energy systems. Recent advances in computing and derivative-free optimization have enabled more tractable solution methods for complex IDC problems that involve, e.g., multi-period dynamics, the presence of high-variance and non-stationarity probabilistic uncertainties, and mixed-integer control/scheduling decisions. Parallelly, developments in techno-ecological synergy (TES) have allowed co-design of industrial and environmental systems that have been shown to lead to win-win solutions in terms of the economy, ecological, and societal benefits. In this work, we propose to combine the IDC and TES frameworks to more accurately capture the real-time interactions between process systems and the surrounding natural resources (e.g., forests, watersheds). Specifically, we take advantage of (multi-scale) model predictive control to close the loop on a realis... [more]

In the regeneration mode, precise control of the Diesel Oxidation Catalyst (DOC) outlet temperature is crucial for the complete combustion of carbon Particulate Matter (PM) in the subsequent Diesel Particulate Filter (DPF) and the effective conversion of Nitrogen Oxides (NOx) in the Selective Catalytic Reduction (SCR). The temperature elevation process of the DOC involves a series of intricate physicochemical reactions characterized by high nonlinearity, substantial time delays, and uncertainties. These factors render effective and stable control of the DOC outlet temperature challenging. To address these issues, this study proposes an approach based on Long Short-Term Memory (LSTM) neural networks for Model Predictive Control (MPC), emphasizing precise control of the Diesel Oxidation Catalyst’s outlet temperature during the regeneration mode. To tackle the system’s nonlinear characteristics, LSTM is employed to construct a predictive model for the outlet temperature of the Diesel Oxid... [more]

In order to address the problem of the real-time scheduling and control of batch chemical systems, this work proposes a model predictive control method based on Petri nets. First, a method is presented to construct a batch chemical system’s timed Petri net model. Second, a control structure is designed to augment the Petri net model to control the valves. This results in timed Petri nets that formally represent the process specifications of a batch chemical system. Third, a model predictive control method is developed to schedule and control timed Petri nets, where a proposed heuristic function is utilized to perform the optimization computation. The model parameters are dynamically adjusted using online data, and both scheduling and valve control instructions are calculated in real time. Finally, a series of experiments is carried out in a beer canning plant to verify the proposed method. According to the experimental results, the scheduling and control problem can be solved in real t... [more]

This work focuses on developing and applying Encrypted Lyapunov-based Model Predictive Control (LMPC) in a nonlinear chemical process network for Ethylbenzene production. The network, governed by a nonlinear dynamic model, comprises two continuously stirred tank reactors that are connected in series and is simulated using Aspen Plus Dynamics. For enhancing system cybersecurity, the Paillier cryptosystem is employed for encryption−decryption operations in the communication channels between the sensor−controller and controller−actuator, establishing a secure network infrastructure. Cryptosystems generally require integer inputs, necessitating a quantization parameter d, for quantization of real-valued signals. We utilize the quantization parameter to quantize process measurements and control inputs before encryption. Through closed-loop simulations under the encrypted LMPC scheme, where the LMPC uses a first-principles nonlinear dynamical model, we examine the effect of the quantization... [more]

Wave energy converters are attracting attention as an energy source that can respond to climate change. In order to increase the energy efficiency of the wave energy converters, efficient power converters are also required. The efficient converters require operation at a low switching frequency, which increases the weight and volume of the passive components. Therefore, in this paper, the performance of various types of topologies is compared to select the optimal power converter for wave energy converters. In order to cope with the unbalanced operation and unbalanced load of renewable energy, in this paper, the topology of the four-leg type is analyzed centrally. In addition, the analysis was performed by applying the model predictive control that can quickly respond to the rapid energy change of wave energy. In addition, model predictive control was applied to the four-leg converter analyzed in this paper because it is suitable for application to atypical topologies. For performance... [more]

This paper presents an equivalent consumption minimization strategy (ECMS) based on model predictive control for series hybrid electric mine trucks (SHE-MTs), the objective of which is to minimize fuel consumption. Two critical works are presented to achieve the goal. Firstly, to gain the real-time speed trajectory on-line, a speed prediction model is established by utilizing a recurrent neural network (RNN). Specifically, a hybrid optimization algorithm based on the genetic algorithm (GA) and the particle swarm optimization algorithm (PSOA) is used to enhance the prediction precision of the speed prediction model. Then, on this basis, an ECMS based on MPC (ECMS-MPC) is proposed. In this process, to improve the real-time and working condition adaptability of the ECMS-MPC, the power-optimal fuel consumption mapping model of the range extender is established, and the equivalent factor (EF) is real-time adjusted by means of the PSOA. Finally, taking a cement mining road as the research ob... [more]

This paper presents a novel strategy for online parameter estimation in a hybrid active power filter (HAPF). This HAPF makes use of existing capacitor banks which it combines with an active power filter (APF) in order to dynamically compensate reactive power. The equipment is controlled with finite control set model predictive control (FCS-MPC) due to its already well-known fast dynamic response. The HAPF model is similar to a grid-connected LCL-filtered converter, so the direct control of the HAPF current can cause resonances and instabilities. To solve this, indirect control, using the capacitor voltage and the inverter-side current, is applied in the cost function, which creates high dependency between the system parameters and the equipment capability to compensate the load reactive power. This dependency is evaluated by simulations, in which the capacitor bank reactance is shown to be the most sensitive parameter, and, thus, responsible for inaccuracies in the FCS-MPC references.... [more]

Due to today’s technological development and information progress, an increasing number of physical systems have become interconnected and linked together through communication networks, thus resulting in Cyber-Physical Systems (CPSs). Continuous manufacturing, which involves the manufacture of products without interruption, has become increasingly important in many industries, including the pharmaceutical and chemical industries. CPSs can be used to control and monitor the production process, which is essential in enabling continuous manufacturing. This paper is focused on the modeling and control of physical systems required in tablet production using dry granulation. Tablets are a type of oral dosage form that is commonly used in the pharmaceutical industry. They are solid, compressed forms of medication that are formulated to release the active ingredients in a manner that allows for optimal absorption and efficacy. Thus, a model predictive control (MPC) strategy is applied to a pl... [more]

The corn-to-sugar process is difficult to control automatically because of the complex physical and chemical phenomena involved. Because the RNN-LSTN model has been shown to handle long-term time dependencies well, this article focused on the design of a model predictive control system based on this machine learning model. Based on the historical data, we first reduced the input variable dimension through data preprocessing, data dimension reduction, sensitivity analysis, etc., and then the RNN-LSTM model, with these identified key sites as inputs, and the dextrose equivalent value as the output, was constructed. Then, through model predictive control using the locally linearized RNN-LSTM as the predictive model, the objective value of the dextrose equivalent was successfully controlled at the target value by our simulation study, in different situations of setpoint changes and disturbances. This showed the potential of applying RNN-LSTM-Based model predictive control in a corn-to-suga... [more]

The structure of large biomass circulating fluidized bed (BCFB) boilers is complex, and control schemes for coal-fired boilers cannot be simply applied to biomass boilers. Multivariable coupling and operational disturbances are also common issues. In this study, a state space model of a 130 t/h BCFB boiler was established under different operating conditions. Using the 100% operating point as an example, a model predictive controller was designed and tested under output disturbance and input disturbance conditions. The results show that the predictive control system designed in this study has a fast response speed and good stability.

The primary regulation of photovoltaic (PV) systems is a current matter of research in the scientific community. In Grid-Feeding operating mode, the regulation aims to track the maximum power point in order to fully exploit the renewable energy sources and produce the amount of reactive power ordered by a hierarchically superior control level or by the local Distribution System Operator (DSO). Actually, this task is performed by Proportional−Integral−Derivative (PID)-based regulators, which are, however, affected by major drawbacks. This paper proposes a novel control architecture involving advanced control theories, like Model Predictive Control (MPC) and Sliding Mode (SM), in order to improve the overall system performance. A comparison with the conventional PID-based approach is presented and the control theories that display a better performance are highlighted.

Microgrids (MGs) interest is growing very fast due to the environment urgency and their capability to integrate renewable energy in a flexible way. In particular, islanded MGs in which distributed energy resources (DERs) are connected to the infrastructure with power electronic converters have attracted the interest of many researchers of both academia and industry because management, control and protection of such systems is quite different from the case of traditional networks. According to their operation mode, MGs that power electronic converters can be divided into grid-forming, grid-feeding and grid-supporting inverters. In particular, grid forming inverters are asked to impose voltage and frequency in the MG. This paper aims to propose a model predictive control (MPC) based approach for grid-forming inverters in an islanded MG. The MPC strategy is implemented because of its capability to define the optimal control actions that contemporarily track the desired reference signals a... [more]

This research paper presents a model of predictive control with a modulator for the inverter linked to the electrical grid, using the stationary reference frame and operating under grid distorted voltage. The stationary reference frame model for the system is obtained in its fundamental frequency and then the model predictive technique is implemented, which predicts the system actions using the obtained system model without the need of any other harmonic consideration. The controller calculates the voltage vector of the inverter through the minimization of the cost function. Thus, the proposal demonstrates, through experiments, its positive results regarding the low impact of the distorted voltage in the grid current without using any harmonic consideration on the model. Experimental results and comparisons carried out endorse the proposal of this work.

The droop control scheme based on Q − ω and P − V characteristics is conventionally employed to share the load power among sources in an islanded low-voltage microgrid with resistive line impedances. However, it suffers from poor active power sharing, and is vulnerable to sustained deviations in frequency and voltage. Therefore, accurate power sharing and maintaining the frequency and voltage in the desired ranges are challenging. This paper proposes a novel microgrid control strategy to address these issues. The proposed strategy consists of a virtual flux droop and a model predictive control, in which the virtual flux is the time integral of the voltage. Firstly, the novel virtual flux droop control is proposed to accurately control the power sharing among DGs. Then, the model predictive flux control is employed to generate the appropriate switching signals. The proposed strategy is simple without needing multiple feedback control loops. In addition, pulse width modulation is not req... [more]

Finite Control-Set Model Predictive Control (FCS-MPC) appears as an interesting alternative to regulate multiphase electric drives, thanks to inherent advantages such as its capability to include new restrictions and fast-transient response. Nevertheless, in industrial applications, FCS-MPC is typically discarded to control multiphase motors because the absence of a modulation stage produces a high harmonic content. In this regard, multi-vectorial approaches are an innovative solution to improve the electric drive performance taking advantage of the implicit modulator flexibility of Model Predictive Control (MPC) strategies. This work proposes the definition of a new multi-vectorial set of control actions formed by a couple of adjacent large voltage vectors and a null voltage vector with an adaptative application ratio. The combination of two large voltage vectors provides minimum x-y current injection whereas the application of a null voltage vector reduces the active voltage producti... [more]

This paper presents the improvements of the hosting capacity of photovoltaics (PVs) and electric vehicles (EVs) in a stand-alone microgrid (MG) with an energy storage system (ESS) by consider-ing a model predictive control (MPC) based energy management system. The system is configured as an MG, including PVs, an ESS, a diesel generator (DG), and several loads with EVs. The DG is controlled to operate at rated power and the MPC algorithm is used in a stand-alone MG, which supplies the energy demanded for several loads with EVs. The hosting capacity of the load in-cluding the EV and PVs can be expanded through the ESS to the terminal node of the microgrid. In this case, the PVs and the load can be connected in excess of the capacity of the diesel genera-tor, and each bus in the feeder complies with the voltage range required by the grid. The effec-tiveness of the proposed algorithm to resolve the hosting capacity is demonstrated by numerical simulations.

The increasing share of distributed renewable energy resources (DER) in the grid entails a paradigm shift in energy system operation demanding more flexibility on the prosumer side. In this work we show an implementation of linear economic model predictive control (MPC) for flexible microgrid dispatch based on time-variable electricity prices. We focus on small and medium enterprises (SME) where information and communications technology (ICT) is available on an industrial level. Our implementation uses field devices and is evaluated in a hardware-in-the-loop (HiL) test bench to achieve high technological maturity. We use available forecasting techniques for power demand and renewable energy generation and evaluate their influence on energy system operation compared to optimal operation under perfect knowledge of the future and compared to a status-quo operation strategy without control. The evaluation scenarios are based on an extensive electricity price analysis to increase representa... [more]

The ESS is considered as an effective tool for enhancing the flexibility and controllability of a wind farm, and the optimal control scheme of a wind farm with distributed ESSs is vital to the stable operation of wind power generation. In this paper, a coordinated active and reactive power control strategy based on model predictive control (MPC) is proposed for doubly fed induction generator (DFIG)-based wind farm (WF) with distributed energy storage systems (ESSs). The proposed control scheme coordinates the active and reactive power output among DFIG wind turbines (WTs), grid-side converters (GSCs), and distributed ESSs inside the WF, and the aim is to decrease fatigue loads of WTs, make the WT terminal voltage inside the extent practicable, and take the WF economic operation into consideration. Moreover, the best reactive power references of DFIG stator and GSC are produced independently based on their dynamics. At last, the control scheme generates optimal power references for all... [more]

This paper proposes a novel model predictive current control scheme for two-stage matrix converter. The switching frequency is kept constant by fixing the switching instant. The control strategy achieves to control source reactive power in the input side and output currents in the output side. In addition, the advantage of the proposed strategy compared with conventional model predictive control is firstly proved using the principle of vector synthesis and the law of sines in the vector distribution area. Moreover, a zero-current switching sequence is proposed and implemented to insure zero-current switching operations and reduce the switching losses. Furthermore, in order to suppress the input filter resonance, which is easier to be inspired by the model predictive control, compared with traditional control strategies, an innovative active damping technique is proposed and implemented. Finally, both simulation and experiment are implemented to verify the performance of the proposed st... [more]

This paper proposes a vector modulation-based model predictive current control strategy for a two-stage matrix converter. The switching frequency is kept constant by fixing the switching instantly. The control scheme controls the source reactive power on the input side and output currents on the output side. Besides, the advantage of the proposed strategy compared with conventional model predictive control is firstly proved using the principle of vector synthesis and the law of sines in the vector distribution area. Moreover, to ensure zero-current switching operations and reduce the switching losses, an optimal switching sequence is proposed and implemented. Furthermore, considering that the input filter resonance is easier to be inspired by the model predictive control, compared with conventional linear control strategies, an innovative active damping technique is proposed to suppress the input filter resonance. To assess the performance of the proposed method, simulation and experim... [more]

To enhance the energy performance of a central air-conditioning system, an effective control method for the chilled water system is always essential. However, it is a real challenge to distribute exact cooling energy to multiple terminal units in different floors via a complex chilled water network. To mitigate hydraulic imbalance in a complex chilled water system, many throttle valves and variable-speed pumps are installed, which are usually regulated by PID-based controllers. Due to the severe hydraulic coupling among the valves and pumps, the hydraulic oscillation phenomena often occur while using those feedback-based controllers. Based on a data-calibrated water distribution model which can accurately predict the hydraulic behaviors of a chilled water system, a new Model Predictive Control (MPC) method is proposed in this study. The proposed method is validated by a real-life chilled water system in a 22-floor hotel. By the proposed method, the valves and pumps can be regulated saf... [more]

This paper introduces a Model Predictive Control (MPC) strategy for the optimal energy management of a district whose buildings are equipped with vertically placed Building Integrated Photovoltaic (BIPV) systems and Battery Energy Storage Systems (BESS). The vertically placed BIPV systems are able to cover larger areas of buildings’ surfaces, as compared with conventional rooftop PV systems, and reach their peak of production during winter and spring, which renders them suitable for energy harvesting especially in urban areas. Driven by both these relative advantages, the proposed strategy aims to maximize the district’s autonomy from the external grid, which is achieved through the cooperation of interactive buildings. Therefore, the major contribution of this study is the management and optimal cooperation of a group of buildings, each of which is equipped with its own system of vertical BIPV panels and BESS, carried out by an MPC strategy. The proposed control scheme consists of thr... [more]

The sustainable agriculture cultivation in greenhouses is constantly evolving thanks to new technologies and methodologies able to improve the crop yield and to solve the common concerns which occur in protected environments. In this paper, an MPC-based control system has been realized in order to control the indoor air temperature in a high efficiency greenhouse. The main objective is to determine the optimal control signals related to the water mass flow rate supplied by a heat pump. The MPC model allows a predefined temperature profile to be tracked with an energy saving approach. The MPC has been implemented as a multiobjective optimization model that takes into account the dynamic behavior of the greenhouse in terms of energy and mass balances. The energy supply is provided by a ground coupled heat pump (GCHP) and by the solar radiation while the energy losses related to heat transfers across the glazed envelope. The proposed MPC method was applied in a smart innovative greenhouse... [more]