Data fusion strategies to improve prediction accuracy in Crohn ’ s Disease

Dynamics both “good” or anticipated and “bad” or hidden are at the core of any monitoring, control and optimization task in the processing industry. Whether yearly maintenance of equipment, regular optimization of production recipes or the machine related millisecond-scale periodicities present in a continues-tableting systems, proper interpretation and modeling of measurements and data requires an understanding of the engineering principles of the system, and thus the role of time. This insight can be utilized either explicitly via visualization or post-processing of the model parameters, or incorporated implicitly during model-building. Chemometric methods have emerged as valuable tools in this area of research (and practice) as proven by the inclusion of factor models such as PCA and PLS, including the powerful concept of principal or latent modes in many engineering university curriculums and as established subject in process technology literature. Many modeling strategies from literature are however developed on clean, well-behaving, pure batch data or continuesproduction systems, and are at times lacking a practical fundament. Most operations in the processing industry e.g. run in a semi-continuous mode, with many hidden and unknown variations on top of a noisy signal. In this paper I will present some of the unavoidable challenges based on examples from industry, and discuss some suggested solutions developed in our research group for working with real process data. I will cover both implicit and explicit use of the process characteristics, combining “soft model-builder” and “hard engineering” ideas. Via these examples I hope to convey my opinion that a valuable and realistic, working solution can be achieved by combining chemometric model building skills with process understanding. Understanding chemical production processes through PLS path modelling Geert H. van Kollenburg, Jan Gerretzen & Jeroen J. Jansen 1 Radboud University, the Netherlands. g.vankollenburg@science.ru. nl 2 Nouryon; RD&I / Sustainability; Deventer, The Netherlands An important aspect of the transition to industry 4.0 is that (chemical) production processes are closely monitored. The wealth of data obtained by measuring, among other things, pressures, temperatures and concentrations allows for a better understanding of the process itself. Incorporating substantive process knowledge into our statistical analyses may allow for a detailed understanding of the relationships between parts of a process. A natural choice to model relationships between various ‘blocks’ of a process is the PLS path model. Since in each block multiple variables may be measured, the PLS path modelling framework enables us to calculate the effects of each individual variable on the endproduct, while retaining an interpretable process analytical model. Similarly, the PLS path model can aid in deciding on certain changes in a process to guarantee a required endproduct quality. The presented application entails the analysis of a continuous industrial process at Nouryon (previously AkzoNobel Specialty Chemicals). The data comprises approximately 17000 hourly process measurements of pressures, flowrates, concentrations and other process data. The production process itself requires a catalyst which degrades over time. A new batch of catalyst is installed when the previous one is depleted. The total yield from different batches of catalyst is highly variable and one of the main goals of the presented research is to understand this variation. Due to the nature of the production process, the variables can be easily grouped according to the separate parts of the process where they are measured. This multi-block approach is facilitated by the causal ordering of the distinct blocks in the production process. Due to having multiple batches, we can naively cross-validate model parameters across batches and relate differences in model estimates to catalyst lifetime. PARAMO: Enhanced Data Pre-processing in Batch Multivariate Statistical Process Control M. Fuentes-García, J. M. González-Martínez, G. Maciá-Fernández, J. Camacho 1 Dpt. of Signal Theory, Telematics and Communications & CITIC-UGR, University of Granada nmfuentes@ugr.es 2 Shell Global Solutions International B.V. , Shell Technology Centre Amsterdam Since the pioneering works by Nomikos and MacGregor [1], the Batch Multivariate Statistical Process Control (BMSPC) methodology has been extensively revised and a sheer number of alternative monitoring approaches have been suggested. The different approaches vary in the batch data alignment, the pre-processing approach, the data arrangement and/or the type of model used, from two-way to three-way and from linear to non-linear [2]. One of the most accepted pre-processing schemes, referred to as Trajectory Centering and Scaling (TCS), is based on the normalization to zero mean and unit variance around the average trajectory [1]. However, the main drawback of TCS is the inherent increase of the level of uncertainty in the estimation of model parameters [2]. In this work, two main open questions are addressed: i) can the estimation of pre-processing parameters be improved, thereby reducing the parameter instability in the bilinear modeling of batch data? and ii) does the parameter stability have a statistical significant effect on fault detection? We illustrate how to improve parameter estimation whilst maintaining the good properties of TCS. We propose a new pre-processing approach, PARAMO (PARAmeters from More Observations), which uses more observations than TCS to estimate the pre-processing parameters. We assess PARAMO and TCS by using the data generated from the Saccharomyces Cerevisiae cultivation process [3-4]. PARAMO outperforms the established methodology for pre-processing batch data in BMSPC. Using this proposal, both the parameter stability and the monitoring performance are improved. The results of this research work affect a large amount of the monitoring approaches proposed to date, and we advocate that the preprocessing procedure proposed here should be generally applied in BMSPC. [1] P. Nomikos and J. F. MacGregor, “Multivariate SPC Charts for Monitoring Batch Processes”, Technometrics, vol. 37, no. 1, pp. 41–59, 1995. [2] J. M. González-Martínez, J. Camacho, and A. Ferrer, “Bilinear modelling of batch processes. Part III: parameter stability”, Journal of Chemometrics, vol. 28, pp. 10–27, 2013. [3] J. M. González, J. Camacho, and A. Ferrer, “MVBatch: A matlab toolbox for batch process modeling and monitoring”, Chemometrics and Intelligent Laboratory Systems, vol. 183, pp. 122–133, 2018. [4] F. Lei, M. Rotboll, and S. B. Jorgensen, “A biochemically structured model for Saccharomyces cerevisiae”, Journal of Biotechnology, vol. 88, no. 3, pp. 205–221, 2001. ACKNOWLEDGEMENT This research work was partly conducted during a research stay at Shell Global Solutions International B.V. in Amsterdam (the Netherlands). The authors would also like to thank the Ministry of Economy and Competitiveness, and FEDER funding programs for partial financial support through the projects TIN2014-60346-R and TIN2017-83494-R. Embracing seasonal variation in milk composition in feed-forward control of cheese production process Ewa Szymańska, Frank Gielens, Emanuela Cavatorta, Ivo van der Zouwen, Tom van Hengstum FrieslandCampina, Amersfoort, the Netherlands, ewa.szymanska@frieslandcampina.com Seasonal variation in milk composition is a known but not yet fully characterized phenomenon which can strongly affect quality of dairy products. During cheese production different actions are being taken to respond and minimize seasonal changes in cheese and whey quality. However, often these actions are not taking milk composition changes into account and they are feedback control actions purely based on final product quality. In this project we have investigated the relationship between seasonal variation in milk, whey and cheese composition as well as process variables. Firstly, we have collected and connected historical data over last 2 years including infrared spectra of milk, whey and cheese and process variables as temperatures, pressures, process step times etc. Secondly, in exploratory analysis, seasonal differences in each dataset have been investigated by univariate tests and principal component analysis (PCA) and linked together by generalized cannonical analysis (GCA). Even though, seasonal trends are significant part of variation in each dataset, not always seasonal trends are well synchronized between datasets. Thirdly, different prediction models using sequential orthogonalization partial least squares (SO-PLS) have been built including milk, process variables and whey datasets. During model building process both cheese-making knowledge as well as variable selection with help of chemometric tools were used. One of critical quality attributes of whey can be well predicted by infrared raw milk spectra and a small selection of process variables. Obtained model has been statistically validated and it can be deployed to give daily advise on process variables settings based on incoming milk spectra. Importantly, the seasonal variations in advised process settings are very well synchronized with seasonal variations in milk composition. Currently the model is being introduced at one of our cheese plants as an advisory tool for technologists. After successful validation by plant technologists it can be used as in an automated feed-forward control regime.