Integrating AI/ML to Enhance Efficiency in Energy-Intensive Food and Biofuel Plants

In an exclusive interview with Industry Outlook, Nitin Sambhare, Managing Director, Desmet Engineering Center, shares his views on the AI and ML integration in energy-intensive food and biofuel plants, the challenges associated with these integrations, and more. He is a global business leader with over 30 years of experience in leading global teams, heading pan-India teams, and manufacturing operations across various industry verticals & domains.

What progress have food and biofuel plants made in adopting AI/ML technologies like predictive analytics and energy optimization to enhance efficiency and savings?

Integrating AI/ML in industrial plants involves combining data, such as mechanical and process-specific design data, with real-time process parameters. These data sets are integrated through Plant Automation systems. A majority of these plant design-specific data sets are not accessible to the general public. As a result, there are few restrictions on using Gen AI-based solutions to increase productivity in continuous process plant environments. However, when plant design and engineering firms manage to combine plant-specific design data sets through in-house automation and AI & ML expertise with the real-time process parameter from the process plant owners, the use of AI/ML technologies in developing Prediction and optimization models for increasing efficiencies is still feasible. Many of food and biofuel producers have started expressing interest in these precise prediction and optimization models. Although it is a gradual process, new technology takes time to get established, and manufacturers have progressed well in adopting it, particularly for its accuracy and applicability for real-time plant operation.

How are AI solutions, like machine learning algorithms and IOT integration, enhancing equipment reliability and optimizing energy management in food and biofuel plants?

Biggest challenge, the continuous process plant owners face today, is to detect the anomalies in the equipment operation, well in advance so that they can schedule planned maintenance to reduce abrupt plant shutdowns, production losses, and additional maintenance costs. AI technologies can help to develop predictive models based on historical plant data from equipment sensors & real time process parameters and can also detect anomalies and early warning indications of equipment breakdown. By frequently updating these predictive models with new data, machine learning technologies can improve its accuracy and can take care of changes due to wear & tear in the equipment. For better& effective plant maintenance, AI and ML requires integration with advanced automation systems, instrumentation, and process design aspects.

What specific data challenges—such as collection, storage, real-time processing, or integration—do food and biofuel plants face, and how are they addressing them?

The emergence of big data has introduced challenges related to data accessibility, relevance, and security. Relevant datasets for AI/ML applications are essentiallyrequired to be reliable,& accurate and their availability requires adopting the right technologies and the use of appropriate data sources. The challenges in handling multiple technologies and data sources can be addressed by designing a robust automation system. Majority of the plant data resides within the automation system. Hence automation engineers require an understanding of the plant's operational processes to design the automation system with operational needs, minimize the potential for errors, optimize performance, and maintain efficiency throughout the plants.

How have advancements like Edge Computing and AI-driven predictive maintenance improved process monitoring and energy management in food and biofuel sectors?

Storing and retrieving data from various sources in real-time basis is a challenge. It leads to additional hardware, challenges in integrating various 3rd party software, security risks and reduced speed of response.

Edge computing offers a solution for integrating software by minimizing the need to store data in a centralized location. Instead, data is processed closer to its source, significantly reducing data transfer latency. When processing the data locally, it remains within the Level 0 and Level 1 commercial plant automation networks, thereby enhancing security. AI/ML applications are more secure and better integrated, which enables faster and more accurate decision making. In food and biofuel production, real-time monitoring and rapid responses are crucial to maintain operational efficiency and quality.

How do food and biofuel plants address barriers like data incompatibility and system upgrades when merging AI/ML with existing systems, and what solutions have been implemented?

Real-time data sources are integrated through an automation system that enables the process control automation using sensors such as instrumentation, monitoring systems, third-party data, and process parameters. Most existing plants were built with older automation systems designed using the technologies and data sources available at that time. The older systems present challenges in compiling and processing data when advanced data sources are integrated into them. The varying protocols used by older systems, complicate the integration of modern technologies, such as AI and ML, by limiting data accessibility and availability for advanced applications.

Upgrading to modern automation systems facilitates the integration of AI and ML applications, as well as overall improved plant process control, reduced downtime, and enhanced total cost of ownership. The technological advancements significantly reduce the maintenance costs, the carbon footprint, overall efforts to adopt the latest integration protocols, and the requirement for engineering efforts to build or design systems. Some of the food and biofuel plant operators and owners remain hesitant to migrate from legacy systems to the latest systems. The primary barrier is the need for in-depth knowledge and experience in automation and process operationto ensure a seamless, reliable transition to modern systems. Unfortunately, few engineering companies that supply these process plants have in-house automation team that also carries process operation know-how.

How are innovations like quantum computing, neural networks, and advanced machine learning algorithms expected to transform process operations and energy efficiency in food and biofuel plants?

The AI/ML algorithms currently capable for present needs,are insufficient to address future challenges. Already new complex requirements have led to the development of neural networks and quantum computing algorithms in AI/ML. Operator training simulators and process simulators are widely used to train plant operators to manage plant operations efficiently. These simulators are often expensive and not entirely accurate as they are built based on specific data points at the time of their design and are not regularly updated as plant dynamics change over a period of time due to gradual deterioration in the equipment and process hydraulics. AI and ML provides a solution by enabling real-time simulation of the entire plant’s process dynamics and arecan be used to create operator simulators and process simulators that continuously reflect the current state of plant operations, including any changes due to equipment deterioration or process shifts. These simulators can be easily updated or retrained on an ongoing basis, allowing for more accurate, real-time training through the use of AI and ML.