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ABB Ability™ Expert Optimizer improved SO2 emission control at a CEMEX plant in Germany

ABB dramatically improved SO2 emission control and helped to significantly reduce hydrate consumption by 11%

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The Challenge

  • Improving SO2 emissions control at one of CEMEX’s plants located in Rüdersdorf, Germany

 

Benefits

  • Minimized standard deviation and reduced deviation around operating target for daily SO2 emission
  • Reduced overall hydrate consumption in the plant by 11%

A control system is crucial for any modern plant. It is directly responsible for regulating the plant’s stability and the quality of product delivered. The most commonly used system is PID (proportional integral derivative) control. This forms the base level for automation and performs adequately for simple processes. Cement and mining processes are, however, anything but simple. Due to variability in feed and fuel sources, coupled with complex dynamics, manual operators with PID control tend to remain at “safe distances” from process constraints, at the cost of plant profitability. ABB Ability™ Expert Optimizer tackles the complexity of cement and mining processes, by minimizing the effect of variability in feed and fuel sources and then driving the process in the direction that is most attractive commercially.

The installation topology allows it to be deployed both in ABB Ability™ 800xA control system and in third party control systems, making it applicable to any type of control system setup.
Ryan Koorts, Product Manager ABB Ability™ Expert Optimizer
“ABB Ability™ Expert Optimizer and advanced process control deliver significant proven benefits and play a major role in any digitalization initiative or journey”

Challenge, solution, results

Challenge

The CEMEX plant located in Rüdersdorf, Germany, already utilizing ABB’s Expert Optimizer on their RMP, mills and kiln, approached ABB to improve their emissions control system. The ultimate challenge was to reduce deviation around the operating target for daily SO2 emission.

Solution
This was achieved with ABB Ability™ Expert Optimizer, utilizing established advanced process control techniques to reduce emission variability. This allowed the plant to run closer to targets and constraints, ensuring zero emission violations while decreasing unnecessary hydrate overdosing and consumption. 

The controller reduced operator workload by automatically optimizing the short-term exhaust SO2 target based on the current daily average. There are two optimization modes that allow the operator to select either normal or aggressive optimization, based on whether to target the daily average below the limit at the end of the day or within the next 30 minutes. ABB Ability™ Expert Optimizer automatically adjusts the multiple feeder points of lime hydrate to ensure the SO2 and HCL targets are stringently met.

Results
After one year of operation, ABB Ability™ Expert Optimizer not only minimized standard deviation but significantly reduced the deviation around the operating target for daily SO2 emission. ABB Ability™ Expert Optimizer has also helped reduce overall hydrate consumption in the plant by 11%.




Wolfgang Schulz, Plant Operator at CEMEX
“Previously we found it difficult juggling all the hydrate injection points with other pressing plant distractions. Now it is all handled by Expert Optimizer.”

Daily SO2

Δ Target

STD

Eng. Unit

Operator Control

-27

21

mg/Nm3

ABB Ability™ Expert Optimizer

-6

14

mg/Nm3


Imagine you worrying less about emissions control, limit violations and associated hydrate consumption.

The team at CEMEX’s Rüdersdorf site in Germany is already living this reality thanks to ABB Ability Expert Optimizer.
Olaf Huebner, Manager Production at CEMEX
“ABB’s Expert Optimizer has helped reduce overall hydrate consumption in the plant by 11%”

Hisham Abualteen, Production Coordinator at CEMEX
“The operator confidence and utilization of the emissions controller is high at 91%”

About ABB Ability™ Expert Optimizer

ABB Ability™ Expert Optimizer is perpetually licensed software for controlling, stabilizing and optimizing industrial processes. The solution uses linear and non-linear model predictive control, machine learning, and neural networks to find the best-operating conditions to maximize output, and can immediately detect deviations in cement production processes.

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