1,390 research outputs found

    Nonlinear process fault detection and identification using kernel PCA and kernel density estimation

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    Kernel principal component analysis (KPCA) is an effective and efficient technique for monitoring nonlinear processes. However, associating it with upper control limits (UCLs) based on the Gaussian distribution can deteriorate its performance. In this paper, the kernel density estimation (KDE) technique was used to estimate UCLs for KPCA-based nonlinear process monitoring. The monitoring performance of the resulting KPCA–KDE approach was then compared with KPCA, whose UCLs were based on the Gaussian distribution. Tests on the Tennessee Eastman process show that KPCA–KDE is more robust and provide better overall performance than KPCA with Gaussian assumption-based UCLs in both sensitivity and detection time. An efficient KPCA-KDE-based fault identification approach using complex step differentiation is also proposed

    Neural Network-based Hybrid Estimator for Estimating Concentration in Ethylene Polymerization Process: An Applicable Approach

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    Estimation of a monomer concentration of an ethylene polymerization process has been a challenging problem due to its highly nonlinear behavior and interaction among state variables.  Applying of an extended Kalman filter (EKF) to provide the estimates of the concentration based on measured bed temperatures has usually been prone to errors. Here, alternatively, neural network-based hybrid estimators have been developed and classified into three structures which integrating of either EKF or Kalman filter (KF) to neural network (NN) to provide the estimates. The NNs are integrated to provide the estimates’ error or concentration’s estimates corresponding to individual structure for reducing the estimation error. Simulation results have shown that the hybrid estimators can provide good estimates under nominal condition and disturbance cases. However, in dealing with noises, the NN-KF hybrid estimator gives superior robustness with smooth and accurate estimated values

    Indirect monitoring of energy efficiency in a simulated chemical process

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    Abstract. Energy efficiency is an important part of chemical process sustainability. Wasted energy contributes significantly to process costs and overall emissions. Therefore, contributions to improving energy efficiency in chemical processes are of value. The main objective of this thesis is the exploration of indirect energy efficiency monitoring methods and their compilation into a generalized framework. As part of the proposed framework, data-based modelling methods were explored and used to identify a model for estimating energy efficiency in a simulated process. The proposed framework can act as a potential tool in different practical applications with energy efficiency improvements as an objective. As a simulated test process for this thesis, the Tennessee Eastman process was utilized. This process is widely used in research, especially regarding fault diagnosis and control design. The process includes slow dynamics and nonlinearity, providing interesting challenges for research. Even though the process has been studied extensively, the energy efficiency aspect of the process has not been taken into account in research. The results of the thesis show that data-based models provide sufficient accuracy for real-time estimation of energy efficiency for the Tennessee Eastman process. Parts of the proposed framework were tested with the explored methods, but some areas were beyond the scope of this thesis. As such, further research, for example prediction of the energy efficiency horizon, fault diagnosis and advanced process control, could be beneficial.Energiatehokkuuden epäsuora monitorointi simuloidussa kemiallisessa prosessissa. Tiivistelmä. Energiatehokkuus on tärkeä osa kemiallisen teollisuuden kestävyyttä. Energian käytön tehottomuus näkyy merkittävästi kasvavina prosessikustannuksina ja kokonaispäästöinä. Toimet energiatehokkuuden nostamiseksi ovat siksi merkityksellisiä. Diplomityön päätavoitteena on erilaisten epäsuorien energiatehokkuuden seurantamenetelmien tutkiminen ja niiden kokoaminen yleistettävään menetelmäkehykseen. Datapohjaisia mallinnusmenetelmiä tutkitaan osana esitettyä kehystä, ja niitä hyödynnetään energiatehokkuutta arvioivan mallin muodostuksessa. Esitetty menetelmäkehys voi toimia mahdollisena työkaluna erilaisissa käyttökohteissa, joissa energiatehokkuuden parantaminen on päämääränä. Tutkittavana kohteena diplomityössä käytettiin simuloitua Tennessee Eastman prosessimallia. Vaikka prosessia on tutkittu laajasti, energiatehokkuuden tarkempi tarkastelu on jäänyt vajaaksi. Simuloitua prosessidataa hyödynnettiin tässä työssä prosessin energiatehokkuuden mallipohjaisen arvion muodostuksessa. Työssä analysoitiin myös mallinnuksen luotettavuuteen vaikuttavia tekijöitä, kuten opetusdatan rajallisuutta ja siitä seuraavaa mallin ekstrapolointia. Diplomityön tulokset osoittavat, että Tennessee Eastman prosessin energiatehokkuuden reaaliaikainen arviointi datapohjaisilla menetelmillä onnistuu riittävällä tarkkuudella. Esitetyn menetelmäkehyksen osia testattiin tutkituilla menetelmillä, mutta jotkin alueet jäivät työn ulkopuolelle. Tulevaisuuden mahdollisiin tutkimusalueisiin kuuluukin energiatehokkuuden ennustaminen, vikadiagnostiikka ja niitä yhdistävä kehittynyt prosessisäätö

    Plant-Wide Diagnosis: Cause-and-Effect Analysis Using Process Connectivity and Directionality Information

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    Production plants used in modern process industry must produce products that meet stringent environmental, quality and profitability constraints. In such integrated plants, non-linearity and strong process dynamic interactions among process units complicate root-cause diagnosis of plant-wide disturbances because disturbances may propagate to units at some distance away from the primary source of the upset. Similarly, implemented advanced process control strategies, backup and recovery systems, use of recycle streams and heat integration may hamper detection and diagnostic efforts. It is important to track down the root-cause of a plant-wide disturbance because once corrective action is taken at the source, secondary propagated effects can be quickly eliminated with minimum effort and reduced down time with the resultant positive impact on process efficiency, productivity and profitability. In order to diagnose the root-cause of disturbances that manifest plant-wide, it is crucial to incorporate and utilize knowledge about the overall process topology or interrelated physical structure of the plant, such as is contained in Piping and Instrumentation Diagrams (P&IDs). Traditionally, process control engineers have intuitively referred to the physical structure of the plant by visual inspection and manual tracing of fault propagation paths within the process structures, such as the process drawings on printed P&IDs, in order to make logical conclusions based on the results from data-driven analysis. This manual approach, however, is prone to various sources of errors and can quickly become complicated in real processes. The aim of this thesis, therefore, is to establish innovative techniques for the electronic capture and manipulation of process schematic information from large plants such as refineries in order to provide an automated means of diagnosing plant-wide performance problems. This report also describes the design and implementation of a computer application program that integrates: (i) process connectivity and directionality information from intelligent P&IDs (ii) results from data-driven cause-and-effect analysis of process measurements and (iii) process know-how to aid process control engineers and plant operators gain process insight. This work explored process intelligent P&IDs, created with AVEVA® P&ID, a Computer Aided Design (CAD) tool, and exported as an ISO 15926 compliant platform and vendor independent text-based XML description of the plant. The XML output was processed by a software tool developed in Microsoft® .NET environment in this research project to computationally generate connectivity matrix that shows plant items and their connections. The connectivity matrix produced can be exported to Excel® spreadsheet application as a basis for other application and has served as precursor to other research work. The final version of the developed software tool links statistical results of cause-and-effect analysis of process data with the connectivity matrix to simplify and gain insights into the cause and effect analysis using the connectivity information. Process knowhow and understanding is incorporated to generate logical conclusions. The thesis presents a case study in an atmospheric crude heating unit as an illustrative example to drive home key concepts and also describes an industrial case study involving refinery operations. In the industrial case study, in addition to confirming the root-cause candidate, the developed software tool was set the task to determine the physical sequence of fault propagation path within the plant. This was then compared with the hypothesis about disturbance propagation sequence generated by pure data-driven method. The results show a high degree of overlap which helps to validate statistical data-driven technique and easily identify any spurious results from the data-driven multivariable analysis. This significantly increase control engineers confidence in data-driven method being used for root-cause diagnosis. The thesis concludes with a discussion of the approach and presents ideas for further development of the methods

    Process Data Analytics Using Deep Learning Techniques

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    In chemical manufacturing plants, numerous types of data are accessible, which could be process operational data (historical or real-time), process design and product quality data, economic and environmental (including process safety, waste emission and health impact) data. Effective knowledge extraction from raw data has always been a very challenging task, especially the data needed for a type of study is huge. Other characteristics of process data such as noise, dynamics, and highly correlated process parameters make this more challenging. In this study, we introduce an attention-based RNN for multi-step-ahead prediction that can have applications in model predictive control, fault diagnosis, etc. This model consists of an RNN that encodes a sequence of input time series data into a new representation (called context vector) and another RNN that decodes the representation into output target sequence. An attention model integrated to the encoder-decoder RNN model allows the network to focus on parts of the input sequence that are relevant to predicting the target sequence. The attention model is jointly trained with all other components of the model. By having a deep architecture, the model can learn a very complex dynamic system, and it is robust to noise. In order to show the effectiveness of the proposed approach, we perform a comparative study on the problem of catalyst activity prediction, by using conventional machine learning techniques such as Support Vector Regression (SVR)

    Dynamic process modeling and hybrid intelligent control of ethylene copolymerization in gas phase catalytic fluidized bed reactors

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    BACKGROUND: Polyethylene (PE) is the most extensively consumed plastic in the world, and gas phase‐based processes are widely used for its production owing to their flexibility. The sole type of reactor that can produce PE in the gas phase is the fluidized bed reactor (FBR), and effective modeling and control of FBRs are of great importance for design, scale‐up and simulation studies. This paper discusses these issues and suggests a novel advanced control structure for these systems. RESULTS: A unified process modeling and control approach is introduced for ethylene copolymerization in FBRs. The results show that our previously developed two‐phase model is well confirmed using real industrial data and is exact enough to further develop different control strategies. It is also shown that, owing to high system nonlinearities, conventional controllers are not suitable for this system, so advanced controllers are needed. Melt flow index (MFI) and reactor temperature are chosen as vital variables, and intelligent controllers were able to sufficiently control them. Performance indicators show that advanced controllers have a superior performance in comparison with conventional controllers. CONCLUSION: Based on control performance indicators, the adaptive neuro‐fuzzy inference system (ANFIS) controller for MFI control and the hybrid ANFIS–proportional‐integral‐differential (PID) controller for temperature control perform better regarding disturbance rejection and setpoint tracking in comparison with conventional controllers. © 2019 Society of Chemical Industr
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