www.ptreview.co.uk
15
'26
Written on Modified on
Model predictive control technology for whey production optimization
Global food manufacturer deploys Rockwell Automation’s FactoryTalk Analytics PavilionX Model Predictive Control solution to improve whey permeate consistency, reduce losses and support sustainability goals.
www.rockwellautomation.com
Application Area: Advanced Process Control / Industrial Automation
Industry Sector: Food & Beverage / Dairy Processing
A large global food manufacturer implemented Rockwell’s FactoryTalk® Analytics™ PavilionX™ Model Predictive Control (MPC) technology to improve whey permeate product consistency, reduce operational losses, and advance sustainability goals at its U.S. production facility. MPC solutions use advanced analytics models to compute the best control moves to improve performance within given constraints. MPC software helps manufacturers achieve precision by reducing product variability and increasing yield, while enhancing profitability.
Operational constraints in seasonal dairy processing
Industrial manufacturing of whey permeate products often suffers from environmental fluctuations that disrupt thermal processing equipment. A large global food manufacturer faced significant operational challenges at its processing facility in the United States, where seasonal temperature and humidity swings directly caused inconsistent evaporator and spray dryer performance.
During humid summer periods, the production line suffered from severe drying instability, while dry winter months limited the plant's overall capacity. These variable environmental conditions frequently caused high dryer blockages, requiring frequent clean-in-place events. Collectively, these disruptions resulted in severe product variability and accumulated nearly a week of unplanned operational downtime annually, preventing the plant from reliably maximizing its whey powder output.
Predictive models for real-time process stabilization
To overcome these physical and environmental limitations, the manufacturer selected and deployed FactoryTalk Analytics PavilionX Model Predictive Control (MPC) software. This technology solution functions as a continuous optimization layer on top of the facility's existing automation infrastructure, utilizing advanced analytics models to compute the best control adjustments within pre-defined operational boundaries.
The technical features that led to the selection of this platform include its capability to perform real-time optimization of critical quality variables, specifically product moisture and density. The software provides adaptive responses to changing upstream and downstream constraints while automatically balancing dryer conditions to maintain safe operating ranges. This predictive approach allows the facility to modernize its legacy assets and stabilize complex thermal processes without requiring major capital investments in new heavy machinery.
Collaborative deployment and operator integration
The deployment process focused on seamless integration with the existing automation architecture and active workforce engagement. To ensure operational adoption across all shifts, the implementation team incorporated direct feedback from plant operators to refine the human-machine interface.
This collaborative design process resulted in intuitive control screens that allowed operators to monitor the predictive models easily. The software takes over the continuous adjustment of process setpoints, acting as an automated system that maintains stable operating parameters across different shifts and reduces the need for manual operator intervention.
Quantifiable efficiency and throughput results
The transition to model predictive control delivered immediate operational and economic benefits to the processing facility:
- Downtime Reduction: The deployment successfully eliminated 145 hours of lost production time annually by mitigating unplanned dryer blockages.
- Throughput Optimization: Spray dryer operating efficiency improved by as much as 21%.
- Rapid Return on Investment: The entire process control project achieved complete financial payback in less than six months.
- Sustainability and Quality: Tighter control of density and moisture levels minimized product variability, which reduced energy consumption and decreased material rework.
- Resource Allocation: Reducing manual monitoring duties allowed operations teams to focus on higher-value tasks with increased process confidence.
Additional Context
Model Predictive Control differentiates itself from traditional Proportional-Integral-Derivative (PID) control loops by being inherently multivariable. While a standard PID controller reacts to past errors on a single variable, MPC looks ahead by simulating the future state of the process based on historical data patterns and cross-variable interactions. In competitive benchmarking within food manufacturing environments, this multivariable predictive capability allows the system to preemptively counter environmental humidity spikes before they cause physical product clogging, a feat that reactive legacy control systems cannot achieve.
Edited by Romila DSilva, Induportals Editor, with AI assistance.
Model Predictive Control differentiates itself from traditional Proportional-Integral-Derivative (PID) control loops by being inherently multivariable. While a standard PID controller reacts to past errors on a single variable, MPC looks ahead by simulating the future state of the process based on historical data patterns and cross-variable interactions. In competitive benchmarking within food manufacturing environments, this multivariable predictive capability allows the system to preemptively counter environmental humidity spikes before they cause physical product clogging, a feat that reactive legacy control systems cannot achieve.
Edited by Romila DSilva, Induportals Editor, with AI assistance.
