Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Development and Validation of an In‐Line API Quantification Method Using AQbD Principles Based on UV‐Vis Spectroscopy to Monitor and Optimise Continuous Hot Melt Extrusion Process

open access journalA key principle of developing a new medicine is that quality should be built in, with a thorough understanding of the product and the manufacturing process supported by appropriate process controls. Quality by design principles that have been established for the development of drug products/substances can equally be applied to the development of analytical procedures. This paper presents the development and validation of a quantitative method to predict the concentration of piroxicam in Kollidon® VA 64 during hot melt extrusion using analytical quality by design principles. An analytical target profile was established for the piroxicam content and a novel in‐line analytical procedure was developed using predictive models based on UV‐Vis absorbance spectra collected during hot melt extrusion. Risks that impact the ability of the analytical procedure to measure piroxicam consistently were assessed using failure mode and effect analysis. The critical analytical attributes measured were colour (L* lightness, b* yellow to blue colour parameters—in‐process critical quality attributes) that are linked to the ability to measure the API content and transmittance. The method validation was based on the accuracy profile strategy and ICH Q2(R1) validation criteria. The accuracy profile obtained with two validation sets showed that the 95% β‐expectation tolerance limits for all piroxicam concentration levels analysed were within the combined trueness and precision acceptance limits set at ±5%. The method robustness was tested by evaluating the effects of screw speed (150–250 rpm) and feed rate (5–9 g/min) on piroxicam content around 15% w/w. In‐line UV‐Vis spectroscopy was shown to be a robust and practical PAT tool for monitoring the piroxicam content, a critical quality attribute in a pharmaceutical HME process

Spectroscopic and process data fusion :enhanced monitoring of an industrial fermentation

PhD ThesisLarge scale manufacturing of pharmaceutical products is a highly competitive industry in which technological improvements can maintain fine business margins in the face of competition from those with lower manufacturing overheads. Processes in which pharmaceuticals are produced via fermentation are particularly susceptible to large variability and reduced productivity due to natural variation and limited monitoring and control options. The latest monitoring methods offer the potential to understand causes of variation, improve productivity and as a result maintain the competitive edge. Unfortunately the fermentation environment is not conducive to the implementation of instrumentation. This thesis shows how signals from spectral instruments can be enhanced by other process and spectroscopic measurements, to provide on-line measurements of critical broth concentrations traditionally only available from infrequent off-line analysis. Near infrared (NIR) and Mid infra red (MIR) spectral analysis of fermentation broth can provide measurements of key concentrations throughout a batch. The off-line analysis of broth samples is more straightforward but on-line implementation is possible. In the case of on-line implementation, the quality of information is compromised, placing greater demands on the signal interpretation methods. The objective of the thesis was to understand the causes of process variation and compensate for them during batch progression, consequently on-line implementation was essential. The construction of a robust calibration model for individual instruments is the first step in implementation. The traditional strategy is either to use multivariate techniques such as projection to latent structures (PLS) or wavelength selection through genetic algorithms followed by PLS. An alternative approach is developed where a search strategy identifies a limited number of spectral windows (SWS) that are most descriptive of the concentrations of interest. The benefit of using SWS is that problems associated with over-fitting the calibration model construction data are minimised. This is particularly important in a development environment where the number of batches is limited. The random nature of the search strategy of the SWS algorithm results in a range of calibration models. Multiple calibration models are `stacked' to provide improved accuracy and robustness. It is demonstrated that stacking provides an improved prediction capability compared to selecting the single `best' performing model. Finally, developing calibration models for sub-regions of fermentation operation is contrasted with a global model. The improvement in accuracy of measurements from SWS and stacking is significant but errors in the determination of the concentration of some compounds remained significant. To overcome these offsets, a model relating the calibration residuals to on-line process measurements was constructed using PLS. The model was then used to correct the spectral calibration prediction to result in improved determination of broth concentrations. The fermentation monitoring methodology is demonstrated by application to an industrial antibiotic production process. Corrected predictions of product concentration and broth nutrient levels demonstrate that combining multiple information sources is advantageous from a measurement perspective.Engineering and Physical Sciences Research Council(EPSRC): BatchPro: Centre for Process Analysis and Control Technology (CPACT)

Advanced Methods of Power Load Forecasting

This reprint introduces advanced prediction models focused on power load forecasting. Models based on artificial intelligence and more traditional approaches are shown, demonstrating the real possibilities of use to improve prediction in this field. Models of LSTM neural networks, LSTM networks with a SESDA architecture, in even LSTM-CNN are used. On the other hand, multiple seasonal Holt-Winters models with discrete seasonality and the application of the Prophet method to demand forecasting are presented. These models are applied in different circumstances and show highly positive results. This reprint is intended for both researchers related to energy management and those related to forecasting, especially power load

Short-term forecasting of wind energy: A comparison of deep learning frameworks

Wind energy has been recognized as the most promising and economical renewable energy source, attracting increasing attention in recent years. However, considering the variability and uncertainty of wind energy, accurate forecasting is crucial to propel high levels of wind energy penetration within electricity markets. In this paper, a comparative framework is proposed where a suite of long short-term memory (LSTM) recurrent neural networks (RNN) models, inclusive of standard, bidirectional, stacked, convolutional, and autoencoder architectures, are implemented to address the existing gaps and limitations of reported wind power forecasting methodologies. These integrated networks are implemented through an iterative process of varying hyperparameters to better assess their effect, and the overall performance of each architecture, when tackling one-hour to three-hours ahead wind power forecasting. The corresponding validation is carried out through hourly wind power data from the Spanish electricity market, collected between 2014 and 2020. The proposed comparative error analysis shows that, overall, the models tend to showcase low error variability and better performance when the networks are able to learn in weekly sequences. The model with the best performance in forecasting one-hour ahead wind power is the stacked LSTM, implemented with weekly learning input sequences, with an average MAPE improvement of roughly 6, 7, and 49%, when compared to standard, bidirectional, and convolutional LSTM models, respectively. In the case of two to three-hours ahead forecasting, the model with the best overall performance is the bidirectional LSTM implemented with weekly learning input sequences, showcasing an average improved MAPE performance from 2 to 23% when compared to the other LSTM architectures implemented