7 ways model predictive control benefits your food and beverage operation?

Model predictive control insights

• Model predictive control (MPC) is an optimal control technique in which the calculated control actions minimize a cost function for a constrained dynamical system over a finite, receding horizon.
• Common goals for implementing MPC in the food and beverage (F&B) industry include maximizing throughput, minimizing energy consumption, reducing waste and product variability and ensuring product quality and safety.
• Implementing a successful MPC project in the F&B industry requires a systematic approach encompassing clear objectives, thorough assessment, vendor selection, implementation, testing and continuous monitoring.

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The food and beverage (F&B) industry is characterized by a highly competitive landscape, ever-changing consumer demands and stringent regulations, and attention to advanced process controls can help. Plant engineering and operations managers face the challenge of optimizing production processes to minimize waste, reduce energy consumption and ensure product quality. One innovative solution is model predictive control (MPC), a control strategy that can help achieve operational excellence.

Operators can expand understanding of advanced process control by looking at the essential steps required to evaluate and execute a successful MPC project in the F&B industry from the perspective of a control system integrator.

Step 1: Define MPC objectives and scope

Before diving into the technicalities, defining the project’s objectives and scope is crucial. Some common goals for implementing MPC in the F&B industry include:

• Maximizing throughput

• Minimizing energy consumption

• Reducing waste and product variability

• Ensuring product quality and safety.

Once the objectives are clear, identify the specific processes and equipment to be included in the project scope. This step helps you establish a solid foundation for the project and facilitate alignment with stakeholders.

Step 2: Assess the current control system

Perform a thorough evaluation of the existing control system to identify gaps, limitations and potential areas for improvement. This assessment aids in gauging the feasibility and potential benefits of integrating MPC into the plant. Some essential aspects to consider include the following:

• Control system architecture:Determine if the current architecture can support adding an MPC layer. If not, upgrading the control system to a more advanced platform may be necessary.

• Control system performance:分析历史数据和当前基准control system’s performance to identify areas where MPC could provide significant benefits.

• Integration compatibility:Assess the compatibility of the existing control system with MPC software to ensure seamless integration.

Step 3: Select the right MPC technology and vendor

选择合适的MPC技术和供应商is crucial to the success of a project. An MPC project can be complicated and costly if your partner doesn’t have the right knowledge or capabilities. Consider the following factors when making a decision:

• Technical capabilities:Evaluate the vendor’s ability to deliver a customized MPC solution that meets specific requirements.

• Industry expertise:Select a vendor with a proven track record in the F&B industry, demonstrating a deep understanding of unique challenges and needs.

• Scalability:Ensure the chosen MPC technology can be scaled up or down to accommodate future changes in the plant’s operational needs.

• Support and training:Opt for a vendor that offers comprehensive support and training to ensure a smooth transition and long-term success.

Step 4: Develop a detailed project plan for MPC use

After selecting the right MPC technology and vendor, develop a detailed project plan that includes:

• A timeline with milestones for all project stages, from system design and implementation to testing, commissioning and post-project support

• A risk management plan that identifies potential obstacles and mitigation strategies

• A resource allocation plan that outlines the personnel, equipment and financial resources required for each stage of the project.

Step 5: Implement the MPC solution

Work closely with the selected vendor during the implementation phase to ensure that the MPC solution meets the project objectives and integrates seamlessly with existing control systems. Key activities during this phase include:

• System design:Collaborate with the vendor to design a customized MPC solution that addresses the plant’s needs and challenges.

• Data collection and modeling:Gather historical process data to develop an accurate mathematical model of the plant’s dynamics, which is essential for MPC’s predictive capabilities.

• Control strategy development:Design a suitable control strategy based on the developed model that optimizes the plant’s performance according to the defined objectives.

• Software integration:Integrate the MPC software with the existing control system, ensuring all necessary interfaces and communication protocols are in place.

Step 6: Testing and commissioning for MPC

Before deploying the MPC solution in a production environment, it’s vital to conduct rigorous testing and commissioning to ensure its effectiveness and reliability. This phase includes:

• Simulation testing:Validate the performance of the MPC solution in a simulated environment to identify potential issues and fine-tune the control strategy.

• Closed-loop testing:Run the MPC solution in a closed-loop setting within the plant to assess its real-world performance and make any necessary adjustments.

• Operator training:Train plant personnel to use the MPC solution effectively, ensuring a smooth transition and long-term success.

Step 7: Performance monitoring and optimization for MPC

After successful implementation, continuously monitor the MPC solution’s performance to ensure it meets the defined objectives and delivers the desired results. Key activities during this phase include:

• Performance benchmarking:Compare the MPC solution’s performance against predefined benchmarks and the previous control system to assess improvements.

• Data analysis:Analyze process data to identify trends, potential issues and areas for further optimization.

• Optimization:Fine-tune the MPC solution based on the collected data and insights to maximize its effectiveness and achieve optimal plant performance.

• Periodic reviews:Conduct regular reviews with your vendor and plant personnel to discuss the MPC solution’s performance and address any concerns or challenges.

Systematic approach for MPC project includes assessment

Implementing a successful MPC project in the F&B industry requires a systematic approach encompassing clear objectives, thorough assessment, vendor selection, implementation, testing and continuous monitoring. By following these steps, plant engineering and operations managers can unlock the full potential of MPC technology to enhance operational efficiency, reduce waste and ensure product quality in their facilities. With the right planning, execution and partnership with a reliable control system integrator, you can harness the power of MPC to drive your plant’s success in today’s competitive landscape.

Implementing MPC in the F&B industry holds immense value for plant operations by offering advanced process optimization, adaptability and foresight. In this highly competitive and rapidly evolving sector, MPC can be instrumental in enhancing critical variables such as product quality, energy efficiency, throughput and waste reduction. By employing predictive models and adaptive control strategies, MPC enables real-time adjustments to process conditions, accommodating variations in raw materials and ever-changing consumer demands. This powerful technology allows operators to identify and mitigate potential issues before they escalate, ensuring consistent product quality and safety while maximizing overall operational performance. Ultimately, the incorporation of MPC in the F&B industry can substantially improve key performance indicators, directly contributing to increasing profitability, sustainability and competitiveness.

Dan Rileyis analytics manager atInterstates, aControl Engineeringcontent partner. Edited by David Miller, content manager, Control Engineering, CFE Media and Technology,dmiller@cfemedia.com

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Keywords: Model predictive control, food and beverage

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