AI for Industrial Process Control: Intro to Control Strategies

AI for Industrial Process Control: Intro to Control Strategies

In this article series, we’ll dive deep into (1) what these industrial process control systems look like today, (2) how AI can augment them using existing plant data, and (3) what manufacturers and utilities can do today to unlock significant cost saving and process compliance opportunities.

Introduction

Over the past few decades, the “digital revolution” has enabled manufacturers and utilities to equip their plants with distributed and supervisory control systems. Whether its industrial membranes or biological reactors, these control systems lie at the heart of heavy industry automation and enable companies to read, interpret, and use their own machine-generated data to achieve production and compliance targets. Yet despite their universality, these control systems are only recently starting to garner attention as potential candidates for disruption by artificial intelligence (AI).

Today, operators in the control rooms of large plants are expected to rely heavily on their own judgement and experience. While concurrently monitoring dozens of process signals, they are expected to adjust control system settings, troubleshoot alarms, perform quality tests — thereby straining the limits of their human capacity. The good news is that these plants are continuously capturing and storing vast amounts of data that can be readily consumed by an AI system. Using AI for process control, can significantly streamline data processing and empower operators with enhanced decision-support.

In this article series, we’ll dive deep into (1) what these industrial process control systems look like today, (2) how AI can augment them using existing plant data, and (3) what manufacturers and utilities can do today to unlock significant cost saving and process compliance opportunities.

industry-4-0 manufacturing machine-learning artificial-intelligence

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