Control loop optimization in food and beverage manufacturing: small tweaks, big impact

Process optimization is key in food and beverage manufacturing and control loops are the critical components. “Out of tune” loops can affect the quality of the product, the material and energy consumption, and ultimately increase the risk of contamination. This article demonstrates how Industrial AI and Machine Learning can be used for PID loop tuning to improve and optimize control loops to generate big savings and reduce risks.

What is a Control Loop?

A simple form of a process controller is the thermostat which maintains the temperature of a room according to a given setpoint. It operates as a closed loop control device, trying to minimize the difference between the room temperature and the desired one.

The industrial version is the PID (Proportional-Integral-Derivative) control loop - an essential part of every process application. PID loops have been around for a very long time. The first pneumatic instruments featuring a proportional controller were developed by Taylor Instrument Companies at the beginning of the 20th century,

Nowadays, loop controllers are available as standalone devices called single loop controllers, but the most common version is a piece of code that resides in a PLC (Process Logic Controller) or a DCS (Distributed Control System). It makes it easier to combine them to create advanced control diagrams like cascade or feed-forward control, or split range required for the complex control of food & beverage, chemical, oil & gas operations and more.

A lot of literature exists that describe the behavior of PID loops and how to tune them. However, it still represents a challenge for many manufacturers as all the processes are different.

Process complexity is obviously a key factor. Heat jacketed devices such as kettles, dryers, reactors or pasteurization units can be hard to control. When steam is used, the heat transfer is not uniform, which might result in an overshoot during uptimes, making the control loops difficult to tune. Note that this is less prevalent using water. Traditional cascaded loops will only solve part of the problem. An advanced analytics system such as Proficy CSense can help by looking at historical data to create a model of the actual profile and recommend new settings accordingly. The model will take into account the change of parameters such as viscosity and steam pressure which affect the heat transfer coefficient and the flow pattern.

Two apparently similar machines might require different settings as they are equipped with sensors that will react to change in a slightly different way. This might be because they use different technologies – a glass vessel vs a steel vessel, which by nature have different inertia - or simply because their characteristics vary over time; for example, aging valves or deviating sensors.

Loop tuning therefore doesn’t happen once. If done manually it must take place on a regular basis and customized for each asset. Another option is real time monitoring using AI and Machine Learning. 

How can process deviation be corrected more efficiently?

Proficy CSense is an all-in-one solution to determine and understand the causes of process deviation in industrial environments. Its advanced analytics capabilities enable engineers and data scientists to analyze, monitor, predict, simulate, and optimize and control set points in real time.

Proficy CSense includes two sets of capabilities: one for process modeling and troubleshooting, the second for online deployment and real time monitoring.

Data is prepared, visualized, and rules-based, data-driven process models can be constructed. Using these models, root causes of process deviations are identified, so processes can be optimized.

How a major F&B manufacturer is using advanced analytics and data-driven insights to optimize performance

The initial goal of this project was simply to deliver savings in raw materials. With GE Digital's software, this customer connected their machines to collect all the data and model their processes to visualize what was happening. They performed in-depth analyses on their combined raw materials, process and product quality data to understand the correlations and root causes of issues.  This revealed multiple insights into what was impacting the processes and how to improve them.

Using our Proficy CSense analytics solution, they managed to stabilize the production lines and the processes and find the “profit loops” by identifying which control loops were causing problems.

The result was an alignment of processes to where they needed to be, a 20% improvement in OEE, reduced product waste and raw materials costs, and higher quality products.

Try advanced analytics for free without having to be a data scientist

Investing in solutions like Proficy CSense to help you achieve control loop optimization is not expensive. Our customers can achieve a rapid payback on a small outlay. To help you begin your journey, we are currently offering a free trial of CSense and 6 hours of free advanced analytics consulting with every CSense purchase. We invite you to explore these offers.