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

Data fusion strategies to combine sensor and multivariate model outputs for multivariate statistical process control

Process analytical technologies (PAT) applied to process monitoring and control generally provide multiple outputs that can come from different sensors or from different model outputs generated from a single multivariate sensor. This paper provides a contribution to current data fusion strategies for the combination of sensor and/or model outputs in the development of multivariate statistical process control (MSPC) models. Data fusion is explored through three real process examples combining output from multivariate models coming from the same sensor uniquely (in the near-infrared (NIR)-based end point detection of a two-stage polyester production process) or the combination of these outputs with other process variable sensors (using NIR-based model outputs and temperature values in the end point detection of a fluidized bed drying process and in the on-line control of a distillation process). The three examples studied show clearly the flexibility in the choice of model outputs (e.g. key properties prediction by multivariate calibration, process profiles issued from a multivariate resolution method) and the benefit of using MSPC models based on fused information including model outputs towards those based on raw single sensor outputs for both process control and diagnostic and interpretation of abnormal process situations. The data fusion strategy proposed is of general applicability for any analytical or bioanalytical process that produces several sensor and/or model outputs

Methodology for utilising prior knowledge in constructing data-based process monitoring systems with an application to a dearomatisation process

Global competition is forcing the process industry to optimise the production processes. One key factor in optimisation is effective process state monitoring and fault detection. Another motivator to improve process monitoring systems are the substantial losses of revenue resulting from abnormal process conditions. It has been estimated that the petrochemical industry in the US alone loses 20 billion dollars per year because of unoptimal handling of abnormal process situations. Traditionally, the monitoring systems have been based on first principle models, constructed by specialists with process specific expertise. In contrast, the use of data-based modelling methods require less expertise and offers the possibilities to build and update the monitoring models in a short period of time, thus allowing more efficient development of monitoring systems. The aims of this thesis are to augment data-driven modelling with existing process knowledge, to combine different data-based modelling methods, and to utilise calculated variables in modelling in order to improve the accuracy of fault detection and identification (FDI) and to provide all necessary diagnostic information for fault tolerant control. The suggested improvements are included in a methodology for setting up FDI systems. The methodology has been tested by building FDI systems for detecting faults in two online quality analysers in a simulated and in a real industrial dearomatisation process at the Naantali oil refinery (Neste Oil Oyj). In developing an FDI system, background information about the user requirements for the monitoring system is first acquired. The information is then analysed and suitable modelling methods are selected according to the guidelines given in the methodology. Second, the process data are prepared for the modelling methods and augmented with appropriate calculated variables. Next, the input variable sets are determined with the introduced method and the models are constructed. After the estimation accuracy of the models is validated, the values of the fault detection parameters are determined. Finally, the fault detection performance of the system is tested. The system was evaluated during a period of one month at the Naantali refinery in 2007. The monitoring system was able to detect all the introduced analyser faults and to provide the information needed for a fault tolerant control system, thus validating the methodology. The effects of a number of suggested improvements in data-based modelling are analysed by means of a comparison study

Application of multivariate data analysis to improve and optimise industrial processes

EngDABB, who is the sponsoring company for this research work, is a global leader in power and automation technologies based in St. Neots, Cambridgeshire. The thesis discusses the work carried out on a portfolio of projects as a part of the Engineering Doctorate programme. Application of multivariate statistical process control was central to the successful implementation of the projects. The first project focussed on a Process Analytical Technology (PAT) software solution developed by ABB. The US Food and Drug Administration (FDA) have defined PAT as a process for designing, analysing and controlling manufacturing through timely measurements of Critical Quality Attributes (CQAs) of raw and in-process materials in order to achieve final product quality. The project’s overall objective was to enable seamless roll out and maintenance of chemometric models for at-line testing across multiple worldwide locations. The work presented in the thesis discusses a solution that allows global maintenance of at-line analyser measurement stations whilst providing ‘real time’ quality data at the right business level to enable more efficient business decisions. This required optimising the software during the preliminary stages which included developing hierarchical Partial Least Square (PLS) Models, maintaining a process within control and exporting data using the Model Data Exporter plug-in. Likewise the project involved development of a combination of test sets that could assess and improve the robustness of the product. Following the Factory Acceptance Test (FAT) and Site Acceptance Test the product was successfully commissioned at customer site. The second project investigated a recurring uncharacteristic event in the polymerisation process. This unusual phenomenon led to downgrading of the batch further causing a loss of revenue. Previous investigations indicated that the most likely reason for this unusual behaviour was due to the occurrence of crystallisation in the polymerisation reactor. These batches were identified by monitoring a ‘kink’ in the heat up profile during the polymerisation process. The root cause of this crystallisation was initially examined by monitoring the rate of reaction and analysing the behaviour of one variable at a time. However, these approaches were unsuccessful to identify the underlying issue with the crystallised batches. This body of work illustrates a series of steps developed using multivariate analysis techniques to identify unusual batches in the polymer reactor. Exploratory data analysis using Principal Component Analysis (PCA) and Multi-way Principal Component Analysis (MPCA) was performed on the historic batch data (quality, process and Overall Equipment Effectiveness (OEE)) to identify ii the root cause of the problem and develop a well defined method that can be used by the operators to identify abnormal batches

Multi-Rate Observers for Model-Based Process Monitoring

Very often, critical quantities related to safety, product quality and economic performance of a chemical process cannot be measured on line. In an attempt to overcome the challenges caused by inadequate on-line measurements, state estimation provides an alternative approach to reconstruct the unmeasured state variables by utilizing available on-line measurements and a process model. Chemical processes usually possess strong nonlinearities, and involve different types of measurements. It remains a challenging task to incorporate multiple measurements with different sampling rates and different measurement delays into a unified estimation algorithmic framework. This dissertation seeks to present developments in the field of state estimation by providing the theoretical advances in multi-rate multi-delay observer design. A delay-free multi-rate observer is first designed in linear systems under asynchronous sampling. Sufficient and explicit conditions in terms of maximum sampling period are derived to guarantee exponential stability of the observer, using Lyapunov’s second method. A dead time compensation approach is developed to compensate for the effect of measurement delay. Based on the multi-rate formulation, optimal multi-rate observer design is studied in two classes of linear systems where optimal gain selection is performed by formulating and solving an optimization problem. Then a multi-rate observer is developed in nonlinear systems with asynchronous sampling. The input-to-output stability is established for the estimation errors with respect to measurement errors using the Karafyllis-Jiang vector small-gain theorem. Measurement delay is also accounted for in the observer design using dead time compensation. Both the multi-rate designs in linear and nonlinear systems provide robustness with respect to perturbations in the sampling schedule. Multi-rate multi-delay observer is shown to be effective for process monitoring in polymerization reactors. A series of three polycondensation reactors and an industrial gas-phase polyethylene reactor are used to evaluate the observer performance. Reliable on-line estimates are obtained from the multi-rate multi-delay observer through simulation