Ivan Castillo

Big Data Analytics in Chemical Engineering

Big data analytics is the journey to turn data into insights for more informed business and operational decisions. As the chemical engineering community is collecting more data (volume) from different sources (variety), this journey becomes more challenging in terms of using the right data and the right tools (analytics) to make the right decisions in real time (velocity). This article highlights recent big data advancements in five industries, including chemicals, energy, semiconductors, pharmaceuticals, and food, and then discusses technical, platform, and culture challenges. To reach the next milestone in multiplying successes to the enterprise level, government, academia, and industry need to collaboratively focus on workforce development and innovation.

Autocovariance-based plant-model mismatch estimation for linear model predictive control ☆

Abstract In this paper, we present autocovariance-based estimation as a novel methodology for determining plant-model mismatch for multiple-input, multiple-output systems operating under model predictive control. Considering discrete-time, linear time invariant systems under reasonable assumptions, we derive explicit expressions of the autocovariances of the system inputs and outputs as functions of the plant-model mismatch. We then formulate the mismatch estimation problem as a global optimization aimed at minimizing the discrepancy between the theoretical autocovariance estimates and the corresponding values computed from historical closed-loop operating data. Practical considerations related to implementing these ideas are discussed, and the results are illustrated with a chemical process case study.

Advances in big data analytics at The Dow Chemical Company

Big data analytics is the journey to turn data into insights for more informed business and operational decisions. As the Chemical Engineering community is collecting more data (volume) from different sources (variety), this journey becomes more challenging in terms of using the right data and the right tools (analytics) to make the right decisions in real-time (velocity). This paper highlights recent advancements in the big data analytics journey at The Dow Chemical Company in the areas of Enterprise Manufacturing Intelligence, multivariate analysis, on-line fault detection, inferential sensors, and batch data analytics.

Plant-model mismatch estimation in unconstrained state-space MPC

In this paper, we develop an autocovariance-based method for estimating plant-model mismatch in unconstrained model predictive control systems using discrete-time, linear time-invariant state space models. We rely on knowledge of the process noise model, together with other reasonable assumptions, to derive an explicit expression for the autocovariance matrix of the closed-loop outputs. Then, we prove that by minimizing the discrepancy, in a norm sense, between this theoretical value and the autocovariance matrix of the operating data, we find an asymptotically consistent estimate for the plant-model mismatch. We illustrate the use of this approach with a case study.

Profile-driven Features for Offline Quality Prediction in Batch Processes

Abstract The present paper addresses the task of quality prediction in batch processes, where measurements from process variables are used to predict one or more quality variables of interest. The majority of current methods for batch quality prediction are based on complete time profiles for all variables, requiring synchronization before batch-wise unfolding (BWU). Synchronization is complex to implement and requires trained personnel whereas BWU leads to a matrix with thousands of pseudo-variables, increasing the potential for model overfitting. In this context, the development and validation of reliable data-driven predictive models is challenging and time consuming. On the other hand, low complexity approaches for batch processes remain vastly unexplored and only a few examples are available in the literature. Therefore, in this work we present a new methodology called profile-driven features (PdF) for offline quality prediction. PdF presents low modelling and implementation complexity, is able to cope with the dynamics presented by batch process variables and generate useful features for building predictive models. In order to test the proposed method, datasets from two simulated batch processes were obtained and partial least squares models were developed to predict end-of-batch quality parameters. Upon comparison with the benchmark method based on BWU and other feature-oriented approaches, PdF presented similar or superior prediction performances under independent testing conditions, despite its lower complexity.