Data-Driven Control Strategies for the Autonomous Operation of the Pharmaceutical Crystallization Process

This work presents a methodology that relies on the application of the radial basis functions network (RBF)-based feedback control algorithms to a pharmaceutical crystallization process. Within the scope of the model-based evaluation of the proposed strategy, firstly strategies for the data treatment, data structure and the training methods reflecting the possible scenarios in the industry (Moving Window, Growing Window and Golden Batch strategies) were introduced. This was followed by the incorporation of such RBF strategies within a soft sensor application and a nonlinear predictive data-driven control application. The performance of the RBF control strategies was tested for the undisturbed cases as well as in the presence of disturbances in the process. The promising results from both RBF soft sensor control and the RBF predictive control demonstrated great potential of these techniques for the control of the crystallization process. In particular, both Moving Window and Golden Batch strategies performed the best results for an RBF soft sensor, and the Growing Window outperformed the remaining methodologies for predictive control.

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Model-Based and Data-Driven HVAC Control Strategies for Residential Demand Response

IEEE Open Access Journal of Power and Energy ◽

10.1109/oajpe.2021.3075426 ◽

2021 ◽

pp. 1-1

Author(s):

Xiao Kou ◽

Yan Du ◽

Fangxing Li ◽

Hector Pulgar-Painemal ◽

Helia Zandi ◽

...

Keyword(s):

Demand Response ◽

Control Strategies ◽

Data Driven ◽

Model Based ◽

Hvac Control ◽

Residential Demand

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Data-Driven Control Techniques for Renewable Energy Conversion Systems: Wind Turbine and Hydroelectric Plants

Electronics ◽

10.3390/electronics8020237 ◽

2019 ◽

Vol 8 (2) ◽

pp. 237 ◽

Cited By ~ 1

Author(s):

Silvio Simani ◽

Stefano Alvisi ◽

Mauro Venturini

Keyword(s):

Renewable Energy ◽

Model Predictive Control ◽

Energy Conversion ◽

Wind Turbine ◽

Predictive Control ◽

Control Strategies ◽

Data Driven ◽

Control Techniques ◽

Energy Conversion Systems ◽

Hydroelectric Plants

The interest in the use of renewable energy resources is increasing, especially towards wind and hydro powers, which should be efficiently converted into electric energy via suitable technology tools. To this end, data-driven control techniques represent viable strategies that can be employed for this purpose, due to the features of these nonlinear dynamic processes of working over a wide range of operating conditions, driven by stochastic inputs, excitations and disturbances. Therefore, the paper aims at providing some guidelines on the design and the application of different data-driven control strategies to a wind turbine benchmark and a hydroelectric simulator. They rely on self-tuning PID, fuzzy logic, adaptive and model predictive control methodologies. Some of the considered methods, such as fuzzy and adaptive controllers, were successfully verified on wind turbine systems, and similar advantages may thus derive from their appropriate implementation and application to hydroelectric plants. These issues represent the key features of the work, which provides some details of the implementation of the proposed control strategies to these energy conversion systems. The simulations will highlight that the fuzzy regulators are able to provide good tracking capabilities, which are outperformed by adaptive and model predictive control schemes. The working conditions of the considered processes will be also taken into account in order to highlight the reliability and robustness characteristics of the developed control strategies, especially interesting for remote and relatively inaccessible location of many plants.

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Data-Driven Living Spaces’ Heating Dynamics Modeling in Smart Buildings using Machine Learning-Based Identification

Sensors ◽

10.3390/s20041071 ◽

2020 ◽

Vol 20 (4) ◽

pp. 1071 ◽

Cited By ~ 1

Author(s):

Roozbeh Sadeghian Broujeny ◽

Kurosh Madani ◽

Abdennasser Chebira ◽

Veronique Amarger ◽

Laurent Hurtard

Keyword(s):

Machine Learning ◽

Control Strategies ◽

Data Driven ◽

Strong Nonlinearity ◽

Dynamics Modeling ◽

Modeling And Control ◽

Energy Management Systems ◽

Living Space ◽

Modeling Strategy ◽

Dynamic Heating

Modeling and control of the heating feature of living spaces remain challenging tasks because of the intrinsic nonlinear nature of the involved processes as well as the strong nonlinearity of the entailed dynamic parameters in those processes. Although nowadays, adaptive heating controllers represent a crucial need for smart building energy management systems (SBEMS) as well as an appealing perspective for their effectiveness in optimizing energy efficiency, unfortunately, the leakage of models competent in handling the complexity of real living spaces’ heating processes means the control strategies implemented in most SBEMSs are still conventional. Within this context and by considering that the living space’s occupation rate (i.e., by users or residents) may affect the model and the issued heating control strategy of the concerned living space, we have investigated the design and implementation of a data-driven machine learning-based identification of the building’s living space dynamic heating conduct, taking into account the occupancy (by the residents) of the heated space. In fact, the proposed modeling strategy takes advantage, on the one hand, of the forecasting capacity of the time-series of the nonlinear autoregressive exogenous (NARX) model, and on the other hand, from the multi-layer perceptron’s (MLP) learning and generalization skills. The proposed approach has been implemented and applied for modeling the dynamic heating conduct of a real five-floor building’s living spaces located at Senart Campus of University Paris-Est Créteil (UPEC), taking into account their occupancy (by users of this public building). The obtained results assessing the accuracy and addictiveness of the investigated hybrid machine learning-based approach are reported and discussed.

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Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency

Renewable and Sustainable Energy Reviews ◽

10.1016/j.rser.2018.04.013 ◽

2018 ◽

Vol 90 ◽

pp. 742-756 ◽

Cited By ~ 30

Author(s):

Mischa Schmidt ◽

Christer Åhlund

Keyword(s):

Energy Efficiency ◽

Predictive Control ◽

Control Strategies ◽

Cyber Physical Systems ◽

Data Driven ◽

Smart Buildings ◽

Physical Systems

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Data-Driven Predictive Control of Building Energy Consumption under the IoT Architecture

Wireless Communications and Mobile Computing ◽

10.1155/2020/8849541 ◽

2020 ◽

Vol 2020 ◽

pp. 1-20

Author(s):

Ji Ke ◽

Yude Qin ◽

Biao Wang ◽

Shundong Yang ◽

Hao Wu ◽

...

Keyword(s):

Energy Consumption ◽

Internet Of Things ◽

Energy Management ◽

Predictive Control ◽

Control Strategies ◽

Principal Component ◽

Building Energy ◽

Physical Models ◽

Data Driven ◽

Building Energy Management

Model predictive control is theoretically suitable for optimal control of the building, which provides a framework for optimizing a given cost function (e.g., energy consumption) subject to constraints (e.g., thermal comfort violations and HVAC system limitations) over the prediction horizon. However, due to the buildings’ heterogeneous nature, control-oriented physical models’ development may be cost and time prohibitive. Data-driven predictive control, integration of the “Internet of Things”, provides an attempt to bypass the need for physical modeling. This work presents an innovative study on a data-driven predictive control (DPC) for building energy management under the four-tier building energy Internet of Things architecture. Here, we develop a cloud-based SCADA building energy management system framework for the standardization of communication protocols and data formats, which is favorable for advanced control strategies implementation. Two DPC strategies based on building predictive models using the regression tree (RT) and the least-squares boosting (LSBoost) algorithms are presented, which are highly interpretable and easy for different stakeholders (end-user, building energy manager, and/or operator) to operate. The predictive model’s complexity is reduced by efficient feature selection to decrease the variables’ dimensionality and further alleviate the DPC optimization problem’s complexity. The selection is dependent on the principal component analysis (PCA) and the importance of disturbance variables (IoD). The proposed strategies are demonstrated both in residential and office buildings. The results show that the DPC-LSBoost has outperformed the DPC-RT and other existing control strategies (MPC, TDNN) in performance, scalability, and robustness.

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Comparative Evaluation of Control-Oriented Zone Temperature Prediction Modeling Strategies in Buildings

Volume 2: Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications ◽

10.1115/dscc2015-9864 ◽

2015 ◽

Cited By ~ 4

Author(s):

Venkatesh Chinde ◽

Jeffrey C. Heylmun ◽

Adam Kohl ◽

Zhanhong Jiang ◽

Soumik Sarkar ◽

...

Keyword(s):

Comparative Evaluation ◽

Performance Monitoring ◽

Control Strategies ◽

Critical Role ◽

Energy Resource ◽

Real Data ◽

Data Driven ◽

Monitoring And Control ◽

Test Bed ◽

Occupant Comfort

Predictive modeling of zone environment plays a critical role in developing and deploying advanced performance monitoring and control strategies for energy usage minimization in buildings while maintaining occupant comfort. The task remains extremely challenging, as buildings are fundamentally complex systems with large uncertainties stemming from weather, occupants, and building dynamics. Over the past few years, purely data-driven various control-oriented modeling techniques have been proposed to address different requirements, such as prediction accuracy, flexibility, computation and memory complexity. In this context, this paper presents a comparative evaluation among representative methods of different classes of models, such as first principles driven (e.g., lumped parameter autoregressive models using simple physical relationships), data-driven (e.g., artificial neural networks, Gaussian processes) and hybrid (e.g., semi-parametric). Apart from quantitative metrics described above, various qualitative aspects such as cost of commissioning, robustness and adaptability are discussed as well. Real data from Iowa Energy Center’s Energy Resource Station (ERS) test bed is used as the basis of evaluation presented here.

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Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning

10.20944/preprints202201.0050.v1 ◽

2022 ◽

Author(s):

Ricardo Vinuesa ◽

Oriol Lehmkuhl ◽

Adrian Lozano-Duran ◽

Jean Rabault

Keyword(s):

Reinforcement Learning ◽

Flow Control ◽

Flat Plate ◽

Turbulent Flows ◽

Control Strategies ◽

Data Driven ◽

Crucial Aspect ◽

Aerodynamic Efficiency ◽

Turbulence Simulation ◽

Novel Approaches

In this review we summarize existing trends of flow control used to improve the aerodynamic efficiency of wings. We first discuss active methods to control turbulence, starting with flat-plate geometries and building towards the more complicated flow around wings. Then, we discuss active approaches to control separation, a crucial aspect towards achieving high aerodynamic efficiency. Furthermore, we highlight methods relying on turbulence simulation, and discuss various levels of modelling. Finally, we thoroughly revise data-driven methods, their application to flow control, and focus on deep reinforcement learning (DRL). We conclude that this methodology has the potential to discover novel control strategies in complex turbulent flows of aerodynamic relevance.

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Towards Data-Driven Vehicle Estimation for Signalised Intersections in a Partially Connected Environment

Sensors ◽

10.3390/s21248477 ◽

2021 ◽

Vol 21 (24) ◽

pp. 8477

Author(s):

Roozbeh Mohammadi ◽

Claudio Roncoli

Keyword(s):

Traffic Control ◽

Partial Information ◽

Control Strategies ◽

Synthetic Data ◽

Real Data ◽

Significant Loss ◽

Data Driven ◽

Estimation Methods ◽

Connected Vehicles ◽

Vehicle Data

Connected vehicles (CVs) have the potential to collect and share information that, if appropriately processed, can be employed for advanced traffic control strategies, rendering infrastructure-based sensing obsolete. However, before we reach a fully connected environment, where all vehicles are CVs, we have to deal with the challenge of incomplete data. In this paper, we develop data-driven methods for the estimation of vehicles approaching a signalised intersection, based on the availability of partial information stemming from an unknown penetration rate of CVs. In particular, we build machine learning models with the aim of capturing the nonlinear relations between the inputs (CV data) and the output (number of non-connected vehicles), which are characterised by highly complex interactions and may be affected by a large number of factors. We show that, in order to train these models, we may use data that can be easily collected with modern technologies. Moreover, we demonstrate that, if the available real data is not deemed sufficient, training can be performed using synthetic data, produced via microscopic simulations calibrated with real data, without a significant loss of performance. Numerical experiments, where the estimation methods are tested using real vehicle data simulating the presence of various penetration rates of CVs, show very good performance of the estimators, making them promising candidates for applications in the near future.

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