Model-Based Evaluation of a Data-Driven Control Strategy: Application to Ibuprofen Crystallization

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 Soft Sensor Model Based on Deep Learning for Quality Prediction of Industrial Processes

SN Computer Science ◽

10.1007/s42979-020-00440-4 ◽

2021 ◽

Vol 2 (1) ◽

Author(s):

Xianglin Zhu ◽

Khalil Ur Rehman ◽

Wang Bo ◽

Muhammad Shahzad ◽

Ahmad Hassan

Keyword(s):

Deep Learning ◽

Soft Sensor ◽

Data Driven ◽

Industrial Processes ◽

Quality Prediction ◽

Model Based ◽

Sensor Model

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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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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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Data-driven latent-variable model-based predictive control for continuous processes

Journal of Process Control ◽

10.1016/j.jprocont.2010.08.002 ◽

2010 ◽

Vol 20 (10) ◽

pp. 1207-1219 ◽

Cited By ~ 33

Author(s):

D. Laurí ◽

J.A. Rossiter ◽

J. Sanchis ◽

M. Martínez

Keyword(s):

Predictive Control ◽

Latent Variable ◽

Latent Variable Model ◽

Data Driven ◽

Variable Model ◽

Continuous Processes ◽

Model Based

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Achieving Improved Personalization and Energy Efficiency in Cohabited Work-Spaces Through Data-Driven Predictive Control

Volume 1: Adaptive/Intelligent Sys. Control; Driver Assistance/Autonomous Tech.; Control Design Methods; Nonlinear Control; Robotics; Assistive/Rehabilitation Devices; Biomedical/Neural Systems; Building Energy Systems; Connected Vehicle Systems; Control/Estimation of Energy Systems; Control Apps.; Smart Buildings/Microgrids; Education; Human-Robot Systems; Soft Mechatronics/Robotic Components/Systems; Energy/Power Systems; Energy Storage; Estimation/Identification; Vehicle Efficiency/Emissions ◽

10.1115/dscc2020-3229 ◽

2020 ◽

Author(s):

Syed Ahsan Raza Naqvi ◽

Zachary Nawrocki ◽

Zaid Bin Tariq ◽

Koushik Kar ◽

Sandipan Mishra

Keyword(s):

Predictive Control ◽

Energy Savings ◽

Control Strategies ◽

Control Policy ◽

Data Driven ◽

Test Bed ◽

Model Learning ◽

Conference Room ◽

Work Spaces ◽

Temperature Preferences

Abstract This paper studies the problem of indoor zone temperature control in shared work-spaces equipped with heterogeneous heating and cooling sources with the goal of increased energy savings and environment personalization. We consider two scenarios to assess the performance of our control strategies. The first scenario requires time-bound pre-cooling/pre-heating of a shared space in preparation for a scheduled activity (Scenario A). The second scenario considers a cohabited work-space where occupants have different temperature preferences (Scenario B). Utilizing an on-campus smart conference room (SCR) as a test-bed, we use data-driven model learning to establish a relationship between the room’s heating, ventilation and cooling (HVAC) operations and the zone temperatures. Next, we use a model predictive control (MPC)-based approach to achieve a desired average temperature while minimizing power consumption (for Scenario A) and minimize the thermal discomfort experienced by individuals based on their temperature preferences (for Scenario B). The experimental results show that for Scenario A, the proposed control policy can save a significant amount of energy and achieve the desired mean temperature in the space fairly accurately. We further note that for Scenario B, the control scheme can achieve a significant spatial differentiation in temperature towards satisfying the occupants’ thermal preferences.

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