Multivariate Statistical Process Monitoring and Control

Abstract

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.