An Optimized Type-2 Self-Organizing Fuzzy Logic Controller Applied in Anesthesia for Propofol Dosing to Regulate BIS

During general anesthesia, anesthesiologists who provide anesthetic dosage traditionally play a fundamental role to regulate Bispectral Index (BIS). However, in this paper, an optimized type-2 Self-Organizing Fuzzy Logic Controller (SOFLC) is designed for Target Controlled Infusion (TCI) pump related to propofol dosing guided by BIS, to realize automatic control of general anesthesia. The type-2 SOFLC combines a type-2 fuzzy logic controller with a self-organizing (SO) mechanism to facilitate online training while able to contend with operational uncertainties. A novel data driven Surrogate Model (SM) and Genetic Programming (GP) based strategy is introduced for optimizing the type-2 SOFLC parameters offline to handle inter-patient variability. A pharmacological model is built for simulation in which different optimization strategies are tested and compared. Simulation results are presented to demonstrate the applicability of our approach and show that the proposed optimization strategy can achieve better control performance in terms of steady state error and robustness

Fault classification in dynamic processes using multiclass relevance vector machine and slow feature analysis

This paper proposes a modifed relevance vector machine with slow feature analysis fault classification for industrial processes. Traditional support vector machine classification does not work well when there are insufficient training samples. A relevance vector machine, which is a Bayesian learning-based probabilistic sparse model, is developed to determine the probabilistic prediction and sparse solutions for the fault category. This approach has the benefits of good generalization ability and robustness to small training samples. To maximize the dynamic separability between classes and reduce the computational complexity, slow feature analysis is used to extract the inner dynamic features and reduce the dimension. Experiments comparing the proposed method, relevance vector machine and support vector machine classification are performed using the Tennessee Eastman process. For all faults, relevance vector machine has a classification rate of 39%, while the proposed algorithm has an overall classification rate of 76.1%. This shows the efficiency and advantages of the proposed method

Instrumentation and control of anaerobic digestion processes: a review and some research challenges

The final publication is available at Springer via http://dx.doi.org/10.1007/s11157-015-9382-6[EN] To enhance energy production from methane or resource recovery from digestate, anaerobic digestion processes require advanced instrumentation and control tools. Over the years, research on these topics has evolved and followed the main fields of application of anaerobic digestion processes: from municipal sewage sludge to liquid mainly industrial then municipal organic fraction of solid waste and agricultural residues. Time constants of the processes have also changed with respect to the treated waste from minutes or hours to weeks or months. Since fast closed loop control is needed for short time constant processes, human operator is now included in the loop when taking decisions to optimize anaerobic digestion plants dealing with complex solid waste over a long retention time. Control objectives have also moved from the regulation of key variables measured online to the prediction of overall process perfor- mance based on global off-line measurements to optimize the feeding of the processes. Additionally, the need for more accurate prediction of methane production and organic matter biodegradation has impacted the complexity of instrumentation and should include a more detailed characterization of the waste (e.g., biochemical fractions like proteins, lipids and carbohydrates)andtheirbioaccessibility andbiodegradability characteristics. However, even if in the literature several methodologies have been developed to determine biodegradability based on organic matter characterization, only a few papers deal with bioaccessibility assessment. In this review, we emphasize the high potential of some promising techniques, such as spectral analysis, and we discuss issues that could appear in the near future concerning control of AD processes.The authors acknowledge the financial support of INRA (the French National Institute for Agricultural Research), the French National Research Agency (ANR) for the "Phycover" project (project ANR-14-CE04-0011) and ADEME for Inter-laboratory assay financial support.Jimenez, J.; Latrille, E.; Harmand, J.; Robles Martínez, Á.; Ferrer Polo, J.; Gaida, D.; Wolf, C.... (2015). Instrumentation and control of anaerobic digestion processes: a review and some research challenges. Reviews in Environmental Science and Biotechnology. 14(4):615-648. doi:10.1007/s11157-015-9382-6S615648144Aceves-Lara CA, Latrille E, Steyer JP (2010) Optimal control of hydrogen production in a continuous anaerobic fermentation bioreactor. 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Plant-Wide Diagnosis: Cause-and-Effect Analysis Using Process Connectivity and Directionality Information

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

Integration of process design and control: A review

There is a large variety of methods in literature for process design and control, which can be classified into two main categories. The methods in the first category have a sequential approach in which, the control system is designed, only after the details of process design are decided. However, when process design is fixed, there is little room left for improving the control performance. Recognizing the interactions between process design and control, the methods in the second category integrate some control aspects into process design. With the aim of providing an exploration map and identifying the potential areas of further contributions, this paper presents a thematic review of the methods for integration of process design and control. The evolution paths of these methods are described and the advantages and disadvantages of each method are explained. The paper concludes with suggestions for future research activities

Decision-making and problem-solving methods in automation technology

The state of the art in the automation of decision making and problem solving is reviewed. The information upon which the report is based was derived from literature searches, visits to university and government laboratories performing basic research in the area, and a 1980 Langley Research Center sponsored conferences on the subject. It is the contention of the authors that the technology in this area is being generated by research primarily in the three disciplines of Artificial Intelligence, Control Theory, and Operations Research. Under the assumption that the state of the art in decision making and problem solving is reflected in the problems being solved, specific problems and methods of their solution are often discussed to elucidate particular aspects of the subject. Synopses of the following major topic areas comprise most of the report: (1) detection and recognition; (2) planning; and scheduling; (3) learning; (4) theorem proving; (5) distributed systems; (6) knowledge bases; (7) search; (8) heuristics; and (9) evolutionary programming

Multivariate Statistical Process Monitoring and Control

Application of statistical methods in monitoring and control of industrially significant processes are generally known as statistical process control (SPC). Since most of the modern day industrial processes are multivariate in nature, multivariate statistical process control (MVSPC), supplanted univariate SPC techniques. MVSPC techniques are not only significant for scholastic pursuit; it has been addressing industrial problems in recent past. . Monitoring and controlling a chemical process is a challenging task because of their multivariate, highly correlated and non-linear nature. Present work based on successful application of chemometric techniques in implementing machine learning algorithms. Two such chemometric techniques; principal component analysis (PCA) & partial least squares (PLS) were extensively adapted in this work for process identification, monitoring & Control. PCA, an unsupervised technique can extract the essential features from a data set by reducing its dimensionality without compromising any valuable information of it. PLS finds the latent variables from the measured data by capturing the largest variance in the data and achieves the maximum correlation between the predictor and response variables even if it is extended to time series data. In the present work, new methodologies; based on clustering time series data and moving window based pattern matching have been proposed for detection of faulty conditions as well as differentiating among various normal operating conditions of Biochemical reactor, Drum-boiler, continuous stirred tank with cooling jacket and the prestigious Tennessee Eastman challenge processes. Both the techniques emancipated encouraging efficiencies in their performances. The physics of data based model identification through PLS, and NNPLS, their advantages over other time series models like ARX, ARMAX, ARMA, were addressed in the present dissertation. For multivariable processes, the PLS based controllers offered the opportunity to be designed as a series of decoupled SISO controllers. For controlling non-linear complex processes neural network based PLS (NNPLS) controllers were proposed. Neural network; a supervised category of data based modeling technique was used for identification of process dynamics. Neural nets trained with inverse dynamics of the process or direct inverse neural networks (DINN) acted as controllers. Latent variable based DINNS’ embedded in PLS framework termed as NNPLS controllers. (2×2), (3×3), and (4×4) Distillation processes were taken up to implement the proposed control strategy followed by the evaluation of their closed loop performances. The subject plant wide control deals with the inter unit interactions in a plant by the proper selection of manipulated and measured variables, selection of proper control strategies. Model based Direct synthesis and DINN controllers were incorporated for controlling brix concentrations in a multiple effect evaporation process plant and their performances were compared both in servo and regulator mode