Emerson Software Helps Covestro Increase Operational Efficiency and Sustainability

Advanced process control increases polyester production throughput by 1% and improves energy efficiency while reducing water consumption

Global technology and software company Emerson is helping Covestro, a world-leading producer of high-tech polymer materials, to enhance the operational performance and sustainability of its manufacturing facility in Barcelona, Spain. As part of a continuous plant improvement program aimed at reducing emissions from production and external energy sources to net zero by 2035, Covestro has optimized the esterification process used to produce polyester. By implementing Emerson’s DeltaV™ advanced process control software, Covestro has improved the efficiency of polyester production, leading to increased availability and throughput, and reduced water and energy consumption.

Covestro produces polyester resin at its Barcelona facility to be used within plastics manufacturing, but recently faced operational challenges with its batch-based production process. The need to optimize esterification and prevent unreacted glycol from plugging a downstream condenser while minimizing reflux water usage prompted Covestro to seek an improved control solution.

Due to the complexity of the process variables involved, attempts to achieve these improvements through adjustments to the existing batch process control configuration were unsuccessful. Covestro therefore turned to Emerson, and after careful analysis, its consultants recommended the implementation of DeltaV PredictPro, a multivariable, model predictive control (MPC) and optimization software solution, which is part of Emerson’s evolving DeltaV automation platform.

Emerson engineers worked collaboratively with Covestro to seamlessly integrate the MPC software into the existing DeltaV distributed control system that manages the batch process. The MPC utilizes variables, such as temperature and pressure, to manage reflux flow efficiently. The DeltaV system defines limits and performs start and stop operations for the MPC, eliminating the need for operator intervention that could potentially lead to batch variations.

“By collaborating with partners such as Emerson that share our commitment to sustainability, Covestro aims to achieve added value from carbon used through the value chain,” explained Adrian Belda, manufacturing engineer at Covestro. “Model predictive control from Emerson is a tailored solution that provides the optimal control profile for our batch production process. The MPC gives us the flexibility to increase process efficiency, while considering process safety conditions and guaranteeing product quality.”

Since the deployment of the MPC on one of four reactors, Covestro has achieved significant improvements, including better management of steam rectification, which ensures glycol does not negatively impact downstream operations; reduced reflux water use; and significantly shortened reaction duration. These enhancements have not only increased energy efficiency, but by allowing more batches to be completed, will increase throughput by an estimated 1% annually.

“As organizations strive to achieve ambitious sustainability targets while navigating ever-tighter regulations and global competition, they are looking for ways to drive increased efficiency across their operations,” said Claudio Fayad, vice president of technology for Emerson’s process systems and solutions business. “DeltaV PredictPro helps production teams lock in best practices, so they can be sure the efficiencies they have engineered are performed every time, regardless of who is in the control room.”

Covestro is now exploring the possibility of replicating the approach on similar reactors and integrating boiler control into the MPC solution, which would present further opportunities to reduce energy consumption and help meet its sustainability goals.

Further details about how Emerson’s advanced process control solution helped solve this Covestro operational challenge are available here.

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