Key points
Proportional–integral–derivative (PID) controllers are a well-established standard in the process industry, known for being a very robust and reliable means of controlling individual process variables to their setpoints. Nevertheless, we should ask ourselves whether PID is maximising the potential for optimal control.
Operators know that something happening now will cause a disturbance to downstream process 20 minutes later, yet the PID controller downstream will only start acting when the disturbance actually hits it, not earlier.
This wasted opportunity occurs because PID relies on process feedback alone and has no knowledge of disturbances upstream. On top of that, the PID controller cannot handle multiple variables at the same time, making it harder to control product quality.
Enter Advanced Process Control
This is where Advanced Process Control (APC) comes in. APC is a term for an umbrella of advanced control strategies, which often include Model Predictive Control (MPC) technology. MPC uses a mathematical model of the process that describes relationships between multiple inputs and multiple outputs.
These relationships are dynamic (time-based) and answer the question: if I change my input by x, how will the output respond to it over time? This results in a matrix of input/output dynamic models as shown in Figure 1.
The model is built on empirical data from the process, captured during so-called process response testing, where input variables are given small perturbations and the response of the output variables to these changes are recorded.
Predicting the problem before it happens to identify the optimal operating point
The inclusion of the process model helps the MPC to understand how the disturbances happening now will affect the downstream process variables in the future, allowing it to act proactively before the disturbance hits downstream process. As a result, MPC drastically reduces process variability, which is the key to reaching the most profitable operating point.
Now, we have a multi-variable controller which predicts the future and acts on to reduce operator intervention and lower process variance – so what? How does this affect my bottom-line? MPC includes an economic optimiser which solves a linear program (LP) equation.
This economic optimiser tells MPC where the most beneficial operating point is, taking into consideration current process conditions. This results in economic benefits demonstrated in Figure 2. Here, one can see how MPC reduces the variance of important process variables (e.g. product quality) and, as a result, enables the optimiser to push the process closer to its constraint, without violating it. This results in the following:
- Maximised product throughput
- Maximised product yield
- Lower specific energy consumption
Tangible results and adding value to the process
MPC brings tangible results which can be easily measured. This results in very quick return on investment, typically within six months. The benefits can be easily proven by comparing process data snapshot before MPC was installed with data snapshot after MPC installation and calculating the change in relevant KPIs (e.g. product throughput).
There are also less quantifiable advantages of using APC that are equally as important. It stabilises the entire process so it is easier to control, as well as significantly reduces the need for the operator to intervene during changes in process conditions. This means operators can focus on other tasks, including timely responses to process alarms.
Which processes can APC bring most value to? All of them, where the following is experienced:
- Complex process responses
- Interactions between multiple variables
- Long time delays between change in input variable and reaction of the output variable
- Process variables which cannot be measured continuously, e.g. lab data of product quality
There are numerous well-proven APC applications in existence across the entire process industry, including refining, petrochemicals, chemicals, food and beverage and utilities. Most recently we have seen many developments in APC for the pharmaceutical industry.
Advanced Process Control in action
A very good example of APC in action is a spray dryer, often used in the food and beverage industry. It dries a slurry (often a milk product) using heated air to turn it into a powder. The slurry is sprayed from the top of the vessel while air fans push hot air into it, so that the spray turns into powder and falls to the bottom of the dryer.
The biggest challenge in controlling this process is the humidity of the air, which affects the final powder moisture content. Powder moisture is an important product quality measurement because it determines if the product is sellable. Traditionally, spray dryers have been operated to a recipe, i.e. there are input setpoints which guarantee in-specification product quality, no matter what the process conditions are.
The recipes are typically determined based on summer months when the air humidity is high. This results in a sellable product, but also excessive energy use (higher air temperature over-dries the product) and low throughput (low feed rate lowers product moisture).
APC provides a solution which can increase the efficiency of the process. In this example, APC replaces the traditional controls adjusting the feed rate over outlet air temperature to instead use the dryer’s feed rate as an input variable and outlet air temperature as an outlet variable.
APC also controls a host of other variables, such as inlet air temperature, inlet air fan speed to ensure the process runs safely and smoothly. Additionally, it reacts to any changes in ambient air humidity. This is explained in the diagram in Figure 3.
Also included in output variables is a discontinuous signal such as powder moisture, which is measured in the lab. APC has the ability to create an inferential sensor which can predict the quality in-between the lab samples.
Therefore, any changes in ambient air humidity that happen in real time are immediately mitigated against, and result in a minimisation in variance of process outputs. This means you can maximise product feed rate while maintaining the outlet temperature at low constraint and having the powder moisture within specification. If feed rate cannot be increased any further, APC will lower inlet air temperature, which in turn minimise energy consumption.
Overcoming challenges to maximise efficiency with environmental benefits
The spray dryer example is just one application of many where APC can enhance the traditional process in place. Implementing the software improves process profitability by enhancing quality, increasing throughput, and reducing energy usage.
Sustainability targets are in focus now more than ever, so these incremental changes can be crucial for a business. APC can help optimise manufacturing operations and make the performance improvements you need to improve your bottom line.
