Combination of statistical process control (SPC) methods and classification strategies for situation assessment of batch processes

Postprint (published version

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

Application of AI in Chemical Engineering

A major shortcoming of traditional strategies is the fact that solving chemical engineering problems due to the highly nonlinear behavior of chemical processes is often impossible or very difficult. Today, artificial intelligence (AI) techniques are becoming useful due to simple implementation, easy designing, generality, robustness and flexibility. The AI includes various branches, namely, artificial neural network, fuzzy logic, genetic algorithm, expert systems and hybrid systems. They have been widely used in various applications of the chemical engineering field including modeling, process control, classification, fault detection and diagnosis. In this chapter, the capabilities of AI are investigated in various chemical engineering fields

Selection of sensors by a new methodology coupling a classification technique and entropy criteria

Complex industrial processes invest a lot of money in sensors and automation devices to monitor and supervise the process in order to guarantee the production quality and the plant and operators safety. Fault detection is one of the multiple tasks of process monitoring and it critically depends on the sensors that measure the significant process variables. Nevertheless, most of the works on fault detection and diagnosis found in literature emphasis more on developing procedures to perform diagnosis given a set of sensors, and less on determining the actual location of sensors for efficient identification of faults. A methodology based on learning and classification techniques and on the information quantity measured by the Entropy concept, is proposed in order to address the problem of sensor location for fault identification. The proposed methodology has been applied to a continuous intensified reactor, the "Open Plate Reactor (OPR)", developed by Alfa Laval and studied at the Laboratory of Chemical Engineering of Toulouse. The different steps of the methodology are explained through its application to the carrying out of an exothermic reaction

Hybrid modeling of a biorefinery separation process to monitor short-term and long-term membrane fouling

Membrane filtration is commonly used in biorefineries to separate cells from fermentation broths containing the desired products. However, membrane fouling can cause short-term process disruption and long-term membrane degradation. The evolution of membrane resistance over time can be monitored to track fouling, but this calls for adequate sensors in the plant. This requirement might not be fulfilled even in modern biorefineries, especially when multiple, tightly interconnected membrane modules are used. Therefore, characterization of fouling in industrial facilities remains a challenge. In this study, we propose a hybrid modeling strategy to characterize both reversible and irreversible fouling in multi-module biorefinery membrane separation systems. We couple a linear data-driven model, to provide high-frequency estimates of trans-membrane pressures from the available measurements, with a simple nonlinear knowledge-driven model, to compute the resistances of the individual membrane modules. We test the proposed strategy using real data from the world's first industrial biorefinery manufacturing 1,4-bio-butanediol via fermentation of renewable raw materials. We show how monitoring of individual resistances, even when done by simple visual inspection, offers valuable insight on the reversible and irreversible fouling state of the membranes. We also discuss the advantage of the proposed approach, over monitoring trans-membrane pressures and permeate fluxes, from the standpoints of data variability, effect of process changes, interaction between module in multi-module systems, and fouling dynamics

Pseudo-sample based contribution plots: innovative tools for fault diagnosis in kernel-based batch process monitoring

[EN] This article explores the potential of kernel-based methods for fault diagnosis in batch process monitoring by combining Kernel-Principal Component Analysis and three common techniques which permit analyzing batch data by means of bilinear models: variable-wise unfolding, batch-wise unfolding and landmark feature extraction. Gower's idea of pseudo-sample projection is exploited to develop novel tools, the pseudo-sample based contribution plots, for diagnostic purposes. The results show that, when the datasets under study are affected by severe non-linearities, the proposed approach performs better than classical ones.This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI2011-28112-C04-02 and Shell Global Solutions International B.V. (Amsterdam, The Netherlands) under the project PT13698.Vitale, R.; Noord, OED.; Ferrer Riquelme, AJ. (2015). Pseudo-sample based contribution plots: innovative tools for fault diagnosis in kernel-based batch process monitoring. Chemometrics and Intelligent Laboratory Systems. 149:40-52. https://doi.org/10.1016/j.chemolab.2015.09.013S405214

AI-based Diagnostics for Fault Detection and Isolation in Process Equipment Service

Recent industry requires efficient fault discovering and isolation solutions in process equipment service. This problem is a real-world problem of typically ill-defined systems, hard to model, with large-scale solution spaces. Design of precise models is impractical, too expensive, or often non-existent. Support service of equipment requires generating models that can analyze the equipment data, interpreting the past behavior and predicting the future one. These problems pose a challenge to traditional modeling techniques and represent a great opportunity for the application of AI-based methodologies, which enable us to deal with imprecise, uncertain data and incomplete domain knowledge typically encountered in real-world applications. In this paper the state of the art, theoretical background of conventional and AI-based techniques in support of service tasks and illustration of some applications to process equipment service on bio-ethanol production process are shortly described

Multivariate statistical process control charts for batch monitoring of transesterification reactions for biodiesel production based on near-infrared spectroscopy

[EN] This work describes an application of Multivariate Statistical Process Control to monitor soybean oil transesterification. For the development of multivariate control charts, near infrared spectra were acquired in-line during the evolution of ten batches under Normal Operating Conditions. They were then organized in a three-way array (batch × spectral variable × time). This structure was analysed by the two most commonly used approaches to develop batch monitoring schemes for handling such kind of data, referred to as Nomikos-MacGregor (NM) and Wold-Kettaneh-Friden-Holmberg (WKFH), respectively. To assess the performance of the approaches, eight test batches, during which specific interferences were induced, were manufactured. When applied for off-line monitoring, both NM and WKFH correctly pointed out such intentionally produced failures. On the other hand, concerning on-line monitoring, NM exhibited a better fault detection capability than WKFH. Contribution plots were found to highlight the spectral region mostly affected by the disturbances regardless of the modelling strategy resorted to.The authors would like to thank FACEPE/NUQAAPE, CNPq/INCTAA science funding programs for partial financial support. Research fellowships granted by the Brazilian agencies ANP/Petrobras and CNPq are also gratefully acknowledged. This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI2014-55276-C5-1R and Shell Global Solutions International B.V. (Amsterdam, The Netherlands).Sales Figueiredo, R.; Vitale, R.; Pimentel, MF.; De Lima, SM.; Stragevitch, L.; Ferrer, A. (2016). Multivariate statistical process control charts for batch monitoring of transesterification reactions for biodiesel production based on near-infrared spectroscopy. Computers & Chemical Engineering. 94:343-353. doi:10.1016/j.compchemeng.2016.08.013S3433539

Development of Multivariate Statistical Process Control for an industrial prototype wastewater bio-treatment plant

This research analyzes the feasibility of developing a Multivariate Statistical Process Control (MSPC) framework for monitoring and diagnosing a biological wastewater treatment plant. MSPC makes use of historical database of past successful operations as a reference to judge the normality of future operations. The projection method, Principal Component Analysis (PCA), is utilized not only to compress the originally correlated data but also to extract statistically meaningful information, by projecting the multivariate trajectory data onto a lower dimensional space, spanned by the Principal Components (PC s) retained. From the established \u27normal\u27 operation domain, departure of new operating points from that of \u27normal\u27 domain can be detected by the use of several MSPC monitoring plots. The proposed methodology generates monitoring charts by analyzing the process variables gathered in a reference database; new observations are analyzed by contrasting their projections onto the reference PC s space against that of normal, using a variety of monitoring charts. Possible root causes can sometimes be identified when abnormal deviations have been detected. The capability of such MSPC scheme in monitoring and assessing the behavior of new wastewater treatment operations against the reference is illustrated through simulations of the bio-wastewater treatment plant under a variety of operating conditions. The research first reviews the concepts and techniques of MSPC and the Activated Sludge Model No. 1. It then utilizes these techniques in creating the monitoring and diagnosis framework for a wastewater bio-treatment plant using the activated sludge model No. 1 description as the process model. Simulation is carried out using the Matlab (version 4.2c) and Simulink ^ as the programming platform. The MSPC framework is able to detect abnormal process deviations by comparing the projection of new observations onto the principal component subspace to the \u27normal operation\u27 region established from base case data. If current operating points fall inside this region, it implies that the current operation is \u27normal\u27; If they fall or show a trend of migrating toward outside of the region, it implies emergence of abnormal operations. Usually, it is possible to trace back from the abnormal behavior to their assignable causes by analyzing contribution plots. In this study, a reference database is generated based on the simulation of a large number of variations in the process operating conditions in the neighborhood of a nominal operating condition. These variations include: -21% to +21% changes in the influent nitrate concentration, [NO3 ], in the maximum growth rate of the heterotrophic biomass, pm, h, in the half-saturation constant of COD, Kg, [cod] and - 15% to +15% changes in the influent ammonia concentration, [NH4\u27^]. These deviations are defined as \u27normal operation\u27 deviations. Monitoring charts are obtained based on this simulated database. Acceptable regions are identified in these charts as the standards for monitoring all future processes. Three abnormal cases are simulated to validate the established base case PGA model. They represent 1) bigger than normal amount of changes in the operating conditions not affecting the biological model; 2) bigger than normal amount of changes in the bioprocess parameters altering the process model; 3) new biological event causing plant/model mismatch. Analysis results show that the indication of the migration, over time, toward a state of abnormality is clear and direct. Diagnosis is carried out by analyzing the contribution plot for each of the three abnormal cases. Results show that the PCA method can also identify the possible root causes for the observed abnormality. In addition, the interpretation of the principal components provides more insights to the behavior of the process variables. However, important implementation issues remain that must be addressed before it can proved to be effective when brought on line