AI Applications in Process Industries: A Practical Implementation Guide

The conversation around artificial intelligence in manufacturing often focuses on the latest machine learning algorithms and impressive pilot projects. However, successful AI implementation in continuous process industries requires a more fundamental approach. Before deploying sophisticated models, manufacturing organizations must establish solid data foundations, understand their existing automation infrastructure, and make strategic decisions about how to combine traditional engineering knowledge with data-driven methods.

Simon Rogers, a digital transformation consultant at Yokogawa with decades of experience across oil, gas, and petrochemical operations, offers insights into what actually works when implementing AI in process manufacturing. His perspective moves beyond the technology hype to address the practical requirements for delivering measurable value from AI investments.

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Building AI on Solid Automation Foundations

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Process industries have a significant advantage when it comes to AI implementation: they have collected real-time process data in historians and databases for decades. Some mature plants have information stored for 20 or 30 years, representing an enormous resource for machine learning applications. However, having data available does not automatically translate into successful AI deployment.

The most common mistake organizations make is attempting to implement advanced AI capabilities while their basic automation infrastructure remains poorly maintained. There are established levels of automation in industrial operations, starting with basic instrumentation and sensors that measure pressure, temperature, and flow. If these fundamental measurements are inaccurate, everything built on top of them will produce unreliable results.

The same principle applies to basic control systems. Your distributed control systems and regulatory PID controllers need to be properly tuned before adding layers of advanced analytics. Many organizations have existing advanced control systems, such as multivariable predictive control, that were installed years ago but now operate only 50% of the time due to poor maintenance. Getting these existing systems functioning properly often delivers faster returns than implementing new AI projects.

This foundation-first approach may seem obvious, but the excitement around machine learning and digital transformation often leads organizations to skip these essential steps. Before investing heavily in AI capabilities, conduct an honest assessment of your current automation infrastructure. Are your sensors calibrated and functioning correctly? Are your basic controllers properly tuned? Are your existing advanced control systems operating reliably? Addressing these fundamentals creates the solid platform necessary for successful AI implementation.

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Combining First-Principles Models with Machine Learning

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One of the most valuable insights for process industries involves how to approach AI modeling. There are essentially three approaches to process optimization: extending multivariable predictive control with optimization capabilities, using rigorous first-principles models in real-time optimizers, and applying pure machine learning methods to historical data.

Each approach has strengths and limitations. Multivariable control uses statistical models derived from plant step tests, making it somewhat data-driven but limited to linear relationships. Traditional real-time optimizers use rigorous first-principles models that capture nonlinear behavior and respect thermodynamic laws, but they operate on steady-state assumptions and do not learn from historical experience. Pure machine learning approaches can identify patterns in years of historical data but may produce models that violate fundamental physical laws if not carefully constrained.

The most promising approach combines first-principles modeling with machine learning. This hybrid method provides several advantages. The first-principles component ensures that models respect thermodynamics and heat and material balances, preventing physically impossible predictions. The machine learning component enables the system to learn from decades of operational history, identifying successful operating strategies for different feedstocks and conditions that a purely theoretical model would miss.

For example, in work on crude distillation units at oil refineries, combining process simulation with machine learning has enabled energy and emissions reductions while maintaining product quality. The simulation provides the fundamental understanding of how the unit should behave, while machine learning identifies opportunities based on how experienced operators have successfully run the unit under various conditions.

This combined approach also addresses a critical challenge with process data: quality. While machine learning purists might argue that with enough data and reinforcement learning you can essentially rediscover the laws of thermodynamics, this requires enormous datasets. In practical industrial applications, combining domain knowledge encoded in first-principles models with data-driven learning produces better results with the data you actually have available.

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Data Quality and Accessibility Requirements

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Having decades of process data stored in historians sounds like an ideal starting point for AI, but raw data often requires significant work before it becomes useful for analytics. One fundamental issue is that process data should comply with basic laws of thermodynamics and material balances. When you attempt to build models without cleaning this data, you get bad models regardless of how sophisticated your algorithms are.

Data accessibility presents another challenge. Historically, process databases exist at the plant level rather than in centralized cloud environments. This creates several limitations. It restricts who can access the data within large organizations. It limits the computing power available for analysis. It makes deploying machine learning platforms more difficult.

Moving process data to cloud environments enables several capabilities that are difficult to achieve with plant-level systems. You gain access to elastic computing resources that can scale as needed. You can leverage machine learning platforms and open-source tools like Python libraries without complex local installations. You make data available to data scientists and analysts who may not have direct access to plant networks.

Many organizations in process industries have been reluctant to move operational data to the cloud due to security concerns. However, this is changing rapidly as cloud security capabilities have matured and as organizations recognize the limitations of keeping data siloed at individual facilities. Some companies have been providing real-time plant data to supply chain partners via cloud platforms for over 20 years, demonstrating that appropriate security measures make this approach viable.

The key is developing a data strategy that balances security requirements with the need for accessibility. This might involve creating secure cloud environments that provide controlled access to operational data while maintaining appropriate protections for sensitive information.

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Breaking Down Data Silos with Semantic Technologies

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Beyond data quality and accessibility, process industries face challenges with data integration across different systems and standards. Industrial operations generate data from distributed control systems, safety systems, asset management platforms, production management systems, and numerous other sources. Each system may use different naming conventions, measurement units, and data structures.

Semantic web technologies offer a practical approach to addressing these integration challenges. By creating common ontologies that describe the relationships between different data elements, organizations can build bridges between disparate systems without requiring wholesale replacement of existing infrastructure.

For instance, the Open Process Automation standard defines reference models and vocabularies for process control systems. While full implementation of such standards takes time, you can begin applying semantic technologies incrementally. Start by mapping the most critical data elements from your various systems to a common semantic model. This creates a foundation for more sophisticated analytics that need to combine information from multiple sources.

Natural language processing also plays a growing role in making industrial data more accessible. Rather than requiring every analyst to understand the specific tag naming conventions used in your distributed control system, natural language interfaces can translate business questions into appropriate data queries. This democratizes access to operational data and enables domain experts who understand the process to extract insights without deep technical knowledge of your data infrastructure.

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Implementation Steps for Process Manufacturing Leaders

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Based on these insights, here are practical steps for implementing AI in process manufacturing environments:

Assess your automation foundation: Before pursuing AI projects, evaluate the health of your basic instrumentation, control systems, and existing advanced control applications. Identify quick wins from improving existing capabilities.

Develop a hybrid modeling strategy: Plan to combine first-principles process knowledge with machine learning rather than pursuing purely data-driven approaches. This respects the fundamental physics of your processes while leveraging historical experience.

Prioritize data quality: Invest in cleaning and validating your process data, particularly ensuring compliance with thermodynamic principles and material balances. Poor quality data will undermine even the most sophisticated algorithms.

Create a cloud data strategy: Develop plans for moving appropriate operational data to secure cloud environments, balancing security requirements with the need for accessibility and advanced analytics capabilities.

Start with high-value use cases: Focus initial AI efforts on applications with clear business value such as safety improvements through anomaly detection, energy and emissions reduction, or enhanced equipment reliability through predictive maintenance.

Build semantic data integration capabilities: Begin mapping critical data elements to common ontologies to enable better integration across systems and more sophisticated cross-functional analytics.

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Building Sustainable AI Capabilities in Process Manufacturing

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Successful AI implementation in process industries is not about deploying the latest algorithms or following technology trends. It requires building on solid automation foundations, combining engineering knowledge with data-driven insights, ensuring data quality and accessibility, and breaking down the silos that prevent effective integration.

The organizations that will gain the most value from AI are those that take this methodical approach. They recognize that machine learning models are only as good as the data and automation infrastructure supporting them. They understand that decades of process engineering knowledge should inform rather than be replaced by data-driven methods. And they invest in making their operational data accessible to the teams who can extract value from it while maintaining appropriate security and governance.

This may be less exciting than headlines about AI breakthroughs, but it is the path to sustainable competitive advantage from artificial intelligence in process manufacturing.

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