Adaptive inferential sensors based on evolving fuzzy models

A new technique to the design and use of inferential sensors in the process industry is proposed in this paper, which is based on the recently introduced concept of evolving fuzzy models (EFMs). They address the challenge that the modern process industry faces today, namely, to develop such adaptive and self-calibrating online inferential sensors that reduce the maintenance costs while keeping the high precision and interpretability/transparency. The proposed new methodology makes possible inferential sensors to recalibrate automatically, which reduces significantly the life-cycle efforts for their maintenance. This is achieved by the adaptive and flexible open-structure EFM used. The novelty of this paper lies in the following: (1) the overall concept of inferential sensors with evolving and self-developing structure from the data streams; (2) the new methodology for online automatic selection of input variables that are most relevant for the prediction; (3) the technique to detect automatically a shift in the data pattern using the age of the clusters (and fuzzy rules); (4) the online standardization technique used by the learning procedure of the evolving model; and (5) the application of this innovative approach to several real-life industrial processes from the chemical industry (evolving inferential sensors, namely, eSensors, were used for predicting the chemical properties of different products in The Dow Chemical Company, Freeport, TX). It should be noted, however, that the methodology and conclusions of this paper are valid for the broader area of chemical and process industries in general. The results demonstrate that well-interpretable and with-simple-structure inferential sensors can automatically be designed from the data stream in real time, which predict various process variables of interest. The proposed approach can be used as a basis for the development of a new generation of adaptive and evolving inferential sensors that can a- ddress the challenges of the modern advanced process industry

Comparison of product quality estimation of propylene polymerization in loop reactors using artificial neural network models

One of the major challenges in polymerization industry is the lack of online instruments to measure polymer end-used properties such as xylene soluble, particle size distribution and melt flow index (MFI). As an alternative to the online instruments and conventional laboratory tests, these properties can be estimated using model based-soft sensor. This paper presents models for soft sensors to measure MFI in industrial polypropylene loop reactors using artificial neural network (ANN) model, serial hybrid neural network (HNN) model and stacked neural network (SNN) model. All models were developed and simulated in MATLAB. The simulation results of the MFI based on the ANN, HNN, and SNN models were compared and analyzed. The MFI was divided into three grades, which are A (10-12g/10 min), B (12-14g/10 min) and C (14-16 g/10 min). The HNN model is the best model in predicting all range of MFI with the lowest root mean square error (RMSE) value, 0.010848, followed by ANN model (RMSE=0.019366) and SNN model (RMSE=0.059132). The SNN model is the best model when tested with each grade of the MFI. It has shown lowest RMSE value for each type of MFI (0.012072 for MFI A, 0.017527 for MFI B and 0.015287 for MFI C), compared to HNN model (0.014916 for MFI A, 0.041402 for MFI B and 0.046437 for MFI C) and ANN model (0.015156 for MFI A, 0.076682 for MFI B, and 0.037862 for MFI C)

Composition Prediction of Debutanizer Column using Neural Network

In oil refining industries, debutanizer column is one of the important unit operations. Debutanizer column is the main column used to produce the main product in oil refinery process. The online composition prediction of top and bottom product of debutanizer column using neural network will be an aid to increase product quality monitoring in oil refining industry. In this work, a single dynamic neural network model is used in order to achieve the objective which is to generate composition prediction online of the top and bottom product of debutanizer column. Neural network is a computing system with several of simple and highly interconnected processing elements that will process information using their dynamic state response to external inputs. It is a software based sensor method or known as “soft sensor” which is a helpful technology that utilizes software techniques to infer the value of important but difficult-to-measure process variables from available process variables which are requisite from physical sensor observation or lab measurements. The neural network development and equation based model for ibutane, i-pentane, n-butane, n-pentane and propane has been obtained. Then, these results will be compared with proportional integral derivatives (PID) controller design to show its supremacy over this method

Role of steady state data reconciliation in process model development

In chemical and hydrocarbon industry operational efficiency is improved by model-based solutions. Historical process data plays an important role in the identification and verification of models utilized by these tools. Since most of the used information are measured values, they are affected by errors influencing the quality of these models. Data reconciliaton aims the reduction of random errors to enhance the quality of data used for model development resulting in more reliable process simulators. This concept is applied to the development and validation of the complex process model and simulator of an industrial hydrogenation system. The results show the applicability of the proposed scheme in industrial environment

Support Vector Machine-based Soft Sensors in the Isomerisation Process

This paper presents the development of soft sensor empirical models using support vector machine (SVM) for the continual assessment of 2,3-dimethylbutane and 2-methylpentane mole percentage as important product quality indicators in the refinery isomerisation process. During the model development, critical steps were taken, including selection and pre-processing of the industrial process data, which are broadly discussed in this paper. The SVM model results were compared with dynamic linear output error model and nonlinear Hammerstein-Wiener model. Evaluation of the developed models on independent data sets showed their reliability in the assessment of the component contents. The soft sensors are to be embedded into the process control system, and serve primarily as a replacement during the process analysers’ failure and service periods. This work is licensed under a Creative Commons Attribution 4.0 International License

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

A Model-Based Framework for the Smart Manufacturing of Polymers

It is hard to point a daily activity in which polymeric materials or plastics are not involved. The synthesis of polymers occurs by reacting small molecules together to form, under certain conditions, long molecules. In polymer synthesis, it is mandatory to assure uniformity between batches, high-quality of end-products, efficiency, minimum environmental impact, and safety. It remains as a major challenge the establishment of operational conditions capable of achieving all objectives together. In this dissertation, different model-centric strategies are combined, assessed, and tested for two polymerization systems. The first system is the synthesis of polyacrylamide in aqueous solution using potassium persulfate as initiator in a semi-batch reactor. In this system, the proposed framework integrates nonlinear modelling, dynamic optimization, advanced control, and nonlinear state estimation. The objectives include the achievement of desired polymer characteristics through feedback control and a complete motoring during the reaction. The estimated properties are close to experimental values, and there is a visible noise reduction. A 42% improvement of set point accomplishment in average is observed when comparing feedback control combined with a hybrid discrete-time extended Kalman filter (h-DEKF) and feedback control only. The 4-state geometric observer (GO) with passive structure, another state estimation strategy, shows the best performance. Besides achieving smooth signal processing, the observer improves 52% the estimation of the final molecular weight distribution when compared with the h-DEKF. The second system corresponds to the copolymerization of ethylene with 1,9-decadiene using a metallocene catalyst in a semi-batch reactor. The evaluated operating conditions consider different diene concentrations and reaction temperatures. Initially, the nonlinear model is validated followed by a global sensitivity analysis, which permits the selection of the important parameters. Afterwards, the most important kinetic parameters are estimated online using an extended Kalman filter (EKF), a variation of the GO that uses a preconditioner, and a data-driven strategy referred as the retrospective cost model refinement (RCMR) algorithm. The first two strategies improve the measured signal, but fail to predict other properties. The RCMR algorithm demonstrates an adequate estimation of the unknown parameters, and the estimates converge close to theoretical values without requiring prior knowledge

Development of a Data-Driven Soft Sensor for Multivariate Chemical Processes Using Concordance Correlation Coefficient Subsets Integrated with Parallel Inverse-Free Extreme Learning Machine

Nonlinearity, complexity, and technological limitations are causes of troublesome measurements in multivariate chemical processes. In order to deal with these problems, a soft sensor based on concordance correlation coefficient subsets integrated with parallel inverse-free extreme learning machine (CCCS-PIFELM) is proposed for multivariate chemical processes. In comparison to the forward propagation architecture of neural network with a single hidden layer, i.e., a traditional extreme learning machine (ELM), the CCCS-PIFELM approach has two notable points. Firstly, there are two subsets obtained through the concordance correlation coefficient (CCC) values between input and output variables. Hence, impacts of input variables on output variables can be assessed. Secondly, an inverse-free algorithm is used to reduce the computational load. In the evaluation of the prediction performance, the Tennessee Eastman (TE) benchmark process is employed as a case study to develop the CCCS-PIFELM approach for predicting product compositions. According to the simulation results, the proposed CCCS-PIFELM approach can obtain higher prediction accuracy compared to traditional approaches

Controle de um reator de polimerização de propeno utilizando filtro de partículas e rede neural

Os materiais poliméricos estão presentes em diversos setores industriais e na vida diária da sociedade, apresentando vantagens como menores custos e maior durabilidade. O polipropileno, obtido pela formação de longas cadeias de monômero de propeno, é uma das oleofinas mais importantes da atualidade, possuindo ampla gama de aplicações. Devido ao forte interesse econômico que desperta, existe uma busca contínua por melhorias em seu processo produtivo. Vários métodos para sua fabricação podem ser encontrados, combinando tecnologias de produção e de catalizadores. Para garantir a segurança, as necessidades e atingir os objetivos das operações, torna-se necessário inserir estruturas para um controle eficaz do processo. Entretanto, sem um bom monitoramento, isto não é possível. Em plantas reais de polimerização, os dispositivos de medição estão sujeitos a incertezas e nem sempre estão disponíveis, ou o equipamento de fato não existe ou seu custo de obtenção/manutenção torna seu uso inviável. Assim, esta dissertação propõe um esquema de sensor virtual baseado em filtro de partículas (FP) e rede neural artificial (RNA), que é aplicado a um reator de polimerização de propeno simulado. Este esquema permite a redução da incerteza e a observação de variáveis latentes por meio do FP. Na sequência, a RNA permite a detecção de propriedades finais do polipropileno a partir dos dados melhorados. A preocupação foi fornecer aos controladores informações mais completas e melhoradas. Os resultados mostraram que o sensor virtual possibilitou melhorias no controle do processo, fornecendo estimativas precisas e tempo de ação consistente com intervalos de amostragens industriais, o que destaca seu potencial para aplicação prática. Palavras-chave: Modelagem e Simulação, Estimação de Estados, Filtro de Partículas, Rede Neural, Controle de Processos, Polimerização. &#8195