Canvass AI Introduces Closed-Loop Optimization With Prescriptive Analytics for Process and Sub-Process Level Production

Industrial AI software leader Canvass AI today announced availability of its real-time closed-loop optimization solution for process and sub-process level production. This capability allows operators and engineers to automate production processes across multiple industries and a wide range of manufacturing processes such as fermentation, distillation, co-generation power and much more.

Humera Malik, CEO, Canvass AI, commented "As Canvass AI introduces Closed-Loop Optimization with Prescriptive Analytics, we're not just unveiling a product - we're driving a transformation. This reflects our commitment to pushing technological boundaries and fostering meaningful change in industrial operations. Supported by our Canvass AI Everywhere framework, this technology signifies a leap into the future where efficiency, intelligence, and operational excellence converge. We're proud to lead this journey and anticipate the positive impact it will bring to industries worldwide. By shortening and automating the cycle from predictive to prescriptive modeling, we're proving a streamlined solution for many horizontal use cases shared by industrials."

The Canvass AI closed-loop optimization solution comprises of predefined data mapping, learning models, configuration files, AI workflows, and a setpoint optimizer. Using this framework engineers and operators can confidently apply virtual control to physical processes allowing them to constantly adapt to changing production conditions. Canvass AI's solution overlays legacy OT investments such as APC and DCS to optimize process performance closer to the operating specification limits to maximize quality and output.

In addition to asset uptime and reliability, industrials can derive better process performance using prescriptive modeling in a repeatable and streamlined way. According to Industry 4.0 researchers several factors are driving a shift to closed-loop AI systems including the need for increased resilience, improving efficiency, autonomous manufacturing, AI models that support continuous learning and improvement, and reducing human error in the manufacturing process.

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