| 1. |
- Udugama, Isuru A., et al.
(author)
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The Role of Big Data in Industrial (Bio)chemical Process Operations
- 2020
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In: Industrial & Engineering Chemistry Research. - : American Chemical Society (ACS). - 0888-5885 .- 1520-5045. ; 59:34, s. 15283-15297
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Journal article (peer-reviewed)abstract
- With the emergence of Industry 4.0 and Big Data initiatives, there is a renewed interest in leveraging the vast amounts of data collected in (bio)chemical processes to improve their operations. The objective of this article is to provide a perspective of the current status of Big-Data-based process control methodologies and the most effective path to further embed these methodologies in the control of (bio)chemical processes. Therefore, this article provides an overview of operational requirements, the availability and the nature of data, and the role of the control structure hierarchy in (bio)chemical processes and how they constrain this endeavor. The current state of the seemingly competing methodologies of statistical process monitoring and (engineering) process control is examined together with hybrid methodologies that are attempting to combine tools and techniques that belong to either camp. The technical and economic considerations of a deeper integration between the two approaches is then explored, and a path forward is proposed.
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| 2. |
- Andersen, Emil B., et al.
(author)
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An easy to use GUI for simulating big data using Tennessee Eastman process
- 2022
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In: Quality and Reliability Engineering International. - : John Wiley & Sons. - 0748-8017 .- 1099-1638. ; 38:1, s. 264-282
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Journal article (peer-reviewed)abstract
- Data-driven process monitoring and control techniques and their application to industrial chemical processes are gaining popularity due to the current focus on Industry 4.0, digitalization and the Internet of Things. However, for the development of such techniques, there are significant barriers that must be overcome in obtaining sufficiently large and reliable datasets. As a result, the use of real plant and process data in developing and testing data-driven process monitoring and control tools can be difficult without investing significant efforts in acquiring, treating, and interpreting the data. Therefore, researchers need a tool that effortlessly generates large amounts of realistic and reliable process data without the requirement for additional data treatment or interpretation. In this work, we propose a data generation platform based on the Tennessee Eastman Process simulation benchmark. A graphical user interface (GUI) developed in MATLAB Simulink is presented that enables users to generate massive amounts of data for testing applicability of big data concepts in the realm of process control for continuous time-dependent processes. An R-Shiny app that interacts with the data generation tool is also presented for illustration purposes. The app can visualize the results generated by the Tennessee Eastman Process and can carry out a standard fault detection and diagnosis studies based on PCA. The data generator GUI is available free of charge for research purposes at https://github.com/dtuprodana/TEP.
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| 3. |
- Andersen, Emil B., et al.
(author)
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Big Data Generation for Time Dependent Processes : The Tennessee Eastman Process for Generating Large Quantities of Process Data
- 2020
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In: 30<sup>th</sup> European Symposium on Computer Aided Process Engineering. - : Elsevier. ; , s. 1309-1314
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Conference paper (peer-reviewed)abstract
- The concept of applying data-driven process monitoring and control techniques on industrial chemical processes is well established. With concepts such as Industry 4.0, Big Data and the Internet of Things receiving attention in industrial chemical production, there is a renewed focus on data-driven process monitoring and control in chemical production applications. However, there are significant barriers that must be overcome in obtaining sufficiently large and reliable plant and process data from industrial chemical processes for the development of data-driven process monitoring and control concepts, specifically in obtaining plant and process data that are required to develop and test data driven process monitoring and control tools without investing significant efforts in acquiring, treating and interpreting the data. In this manuscript a big data generation tool is presented that is based on the Tennessee Eastman Process (TEP) simulation benchmark, which has been specifically designed to generate massive amounts of process data without spending significant effort in setting up. The tool can be configured to carry out a large number of data generation runs both using a graphical user interface (GUI) and through a.CSV file. The output from the tool is a file containing process data for all runs as well as process faults (deviations) that have been activated. This tool enables users to generate massive amounts of data for testing applicability of big data concepts in the realm of process control for continuously operating time dependent processes. The tool is available for all researchers and other parties who are interested.
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| 4. |
- Cabaneros Lopez, Pau, et al.
(author)
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Transforming data to information : A parallel hybrid model for real-time state estimation in lignocellulosic ethanol fermentation
- 2021
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In: Biotechnology and Bioengineering. - : Wiley. - 0006-3592 .- 1097-0290. ; 118:2, s. 579-591
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Journal article (peer-reviewed)abstract
- Operating lignocellulosic fermentation processes to produce fuels and chemicals is challenging due to the inherent complexity and variability of the fermentation media. Real-time monitoring is necessary to compensate for these challenges, but the traditional process monitoring methods fail to deliver actionable information that can be used to implement advanced control strategies. In this study, a hybrid-modeling approach is presented to monitor cellulose-to-ethanol (EtOH) fermentations in real-time. The hybrid approach uses a continuous-discrete extended Kalman filter to reconciliate the predictions of a data-driven model and a kinetic model and to estimate the concentration of glucose (Glu), xylose (Xyl), and EtOH. The data-driven model is based on partial least squares (PLS) regression and predicts in real-time the concentration of Glu, Xyl, and EtOH from spectra collected with attenuated total reflectance mid-infrared spectroscopy. The estimations made by the hybrid approach, the data-driven models and the internal model were compared in two validation experiments showing that the hybrid model significantly outperformed the PLS and improved the predictions of the internal model. Furthermore, the hybrid model delivered consistent estimates even when disturbances in the measurements occurred, demonstrating the robustness of the method. The consistency of the proposed hybrid model opens the doors towards the implementation of advanced feedback control schemes.
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| 5. |
- Lopez, Pau Cabaneros, et al.
(author)
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Towards a digital twin : a hybrid data-driven and mechanistic digital shadow to forecast the evolution of lignocellulosic fermentation
- 2020
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In: Biofuels, Bioproducts and Biorefining. - : Wiley. - 1932-104X .- 1932-1031. ; 14:5, s. 1046-1060
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Journal article (peer-reviewed)abstract
- The high substrate variability and complexity of fermentation media derived from lignocellulosic feedstock affects the concentration profiles and the length of the fermentation. Failing to account for such variability raises operational and scheduling issues and affects the overall performance of these processes. In this work, a hybrid soft sensor was developed to monitor and forecast the evolution of cellulose-to-ethanol fermentation. The soft sensor consisted of two modules (a data-driven model and a kinetic model) connected sequentially. The data-driven module used a partial-least-squares model to estimate the current state of glucose from spectroscopic data. The kinetic model was recursively fitted to known concentrations of glucose to update the long-horizon predictions of glucose, xylose, and ethanol. This combination of real-time data update from an actual fermentation process to a high-fidelity kinetic model constitutes the basis of the digital twin concept and allows for a better real-time understanding of complex inhibition phenomena caused by different inhibitors commonly found in lignocellulosic feedstocks. The soft sensor was experimentally validated with three different cellulose-to-ethanol fermentations and the results suggested that this method is suitable for monitoring and forecasting fermentation when the measurements provide reasonably good estimates of the real state of the system. These results would allow the flexibility of the operation of cellulosic processes to be increased, and would permit the scheduling to be adapted to the inherent variability of such substrates.
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