Bridging the Gap: Aligning Academic Research with Real-World Process Control Needs
The industrial automation sector continues to grapple with a long-standing challenge: bridging theoretical academic research with practical control room implementation. While university laboratories frequently focus on complex mathematical frameworks and abstract control concepts, operating facilities often require straightforward, fundamental methods that deliver long-term reliability and predictable performance. To address this discrepancy, prominent control engineers and automation advocates are outlining specific, practice-directed research priorities aimed at solving concrete operational issues.
A major focus of this push involves improving automated plant diagnostics and model maintenance. Modern process plants rely heavily on inferential models, online estimators, and closed-loop control schemes. However, operating conditions naturally shift over time due to equipment fouling, raw material variability, and mechanical wear. Developing practical techniques to automatically detect steady-state versus transient data conditions—and to flag precisely when inferential or controller models require recalibration—remains a top priority for facility operators seeking to prevent process drift and off-spec production.
Industry practitioners are encouraging academic researchers to focus on several practical technical areas:
-
Unified Simulation and Process Modeling: Industrial engineers frequently utilize separate software packages for dynamic plant simulation, real-time optimization, operator training, and process control design. Research into unified, multi-use modeling architectures could drastically lower engineering overhead and ensure consistent operational data across an asset's entire life cycle.
-
Property Distribution Control: Manufacturing processes such as polymerization, catalytic cracking, and crystallization require strict control over product property distributions (e.g., molecular weight or particle size distribution). Practical control strategies focused on managing these physical distributions directly support higher product quality and reduced batch variability.
-
Batch Endpoint Optimization: Identifying precise batch completion points without relying on time-consuming off-line laboratory testing helps facilities shorten cycle times, minimize raw material waste, and maximize annual throughput across chemical and pharmaceutical operations.
-
Automated Flow Diagram Reconciliation: Utilizing real-time operational data to automatically update process flow diagrams and piping schematics helps maintenance teams keep plant documentation fully aligned with physical piping configurations.
Beyond complex model predictive control paradigms, automation experts emphasize that fundamental control strategies require continued attention. Advanced regulatory control methods—utilizing feedback, feedforward, ratio, and cascade configurations—remain the backbone of daily plant stability. By combining rigorous mathematical validation with accessible regulatory control frameworks, academic research can provide process plants with reliable, field-tested solutions that engineers can deploy with high confidence.
Written by: Arthur Pendelton, a veteran process automation consultant with 18 years of experience optimizing chemical plant control loops and implementing advanced regulatory control strategies.