Thermal Oxidizer Optimization Through Model Predictive Control | AIChE

Thermal Oxidizer Optimization Through Model Predictive Control

Paul Pathasema is a Senior Process Control Engineer with Ascend Performance Materials. He has over 14 years’ experience with process engineering and process control in the specialty chemicals, pulp and paper and petrochemicals industries. He has broad expertise in advanced regulatory and advanced multivariable control, as well as extensive experience designing, implementing and managing Safety Instrumented Systems. He holds a Bachelor of Science degree in Chemical Engineering from Auburn University, as well as a Master of Engineering degree in Chemical Engineering from Tulane University, and is trained as a Six Sigma Black Belt.

Thermal Oxidizer Optimization
Two Model Predictive Controllers were implemented on a Thermal Oxidizer at Ascend Performance Materials using DeltaV’s MPCPro block. These controllers allowed Ascend to optimize firing rates, combustion air usage and heat recovery while maintaining environmental compliance. This made it possible to reduce fuel consumption by more than 10% relative to baseline operation. Prior to MPC implementation, operating conditions for the WHB, including temperatures, excess O2, and combustion air preheat, were manually set by Operations personnel. This often resulted in boiler firing rates that were much higher than necessary to maintain permit limits for carbon monoxide (CO) emissions.

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