Papermaking

Modern paper machines are high-tech products with complex chemical and mechanical processes. Although computer-controlled and monitored by thousands of sensors, certain disruptions are just inevitable due to the high complexity and the vast amount of operational data. aivis helps you to understand and avoid those disruptions.

AI in Papermaking

An autonomous AI for operations data like aivis enables you to realize tremendous savings and improvements along the entire production chain – be it by preventing all kinds of process disruptions, by monitoring critical components with anomaly detection, or by improving the information base through soft sensors.

Prevent known process disruptions

Understand and prevent all kinds of process disruptions that show up regularly and whose occurrences can be found in the historical data of the process.

Monitor the health of critical components

Monitor the health of critical components with anomaly detection to receive timely warnings of emerging malfunctions and avoid unplanned downtimes.

Measure process parameters with soft sensors

Create virtual sensors for critical process parameters that are hard or even impossible to measure and increase transparency and safety.

Preventing known process disruptions

Papermaking is full of high-end equipment and complex chemical and mechanical processes. Although being computer-controlled and monitored by hundreds, sometimes thousands of sensors, certain kinds of disruptions just keep on occurring. The high process complexity and large volumes of operational data make it hard to maintain an overview and initiate necessary countermeasures in good time.

This is where aivis comes into play: The powerful AI for operational data can handle even the most extensive amounts of data of thousands of sensors and autonomously look for root causes and warn in time about arising disruptions.

Just by looking into your data, aivis finds root causes that are yet unknown.

How it works

All aivis requires to start is a hint to recognize all occurrences of the disruption in the past. It is somewhat similar to giving a scent sample to a tracking dog to pick up the scent.

aivis then automated, unbiased, and autonomously searches all historical operational data to identify critical dynamics that usually were present right before the disruption occurred.

Finally, aivis creates a report that allows to understand those dynamics and find suitable countermeasures. Furthermore, it makes a model which detects those dynamics to apply the countermeasures in time.

Example

Avoiding paper breaks

Paper breaks can occur at any time without any warning. The sheet or paper web breaks inside the machine. Subsequently, there are various cleaning and retreading steps to take before production continues. Sheet breaks have a significant negative impact on a paper machine’s productivity.

Due to the high complexity of the process, which is monitored by several thousand sensors, even experienced paper engineers have only limited knowledge of when countermeasure must be initiated to avoid paper breaks, resulting in significantly lowered productivity.

With aivis, you can figure out all the destructive dynamics that lead to the paper break. Furthermore, you can receive warnings about arising breaks, including the current reasons, to timely apply suitable countermeasures.

In general, aivis can do the same for any regularly occurring problem recorded in the historian of the asset in question.

Scalable health monitoring of critical components

Critical components like e.g. proportional valves can be found in large numbers. So, even if one component fails only rarely, their large number makes unplanned process interruptions due to component failure inevitable. This has a negative impact on productivity.

With a suitable monitoring solution based on anomaly detection, impending component failures could be detected and dealt with in a timely manner before they actually occur. But usually creating such monitoring models is difficult and expensive, because a lot of pre-knowledge has to be built into the model to reflect the experiences of skilled operators and monitor the relevant relations. This made the generation of monitor models in large numbers impossible.

With aivis, it get’s a lot easier. The only relevant input for aivis is the signal, that reflects the loss in performance, usually a target-actual comparison. aivis then finds out on its own all relevant relationships to monitor for watching the components health, enabling a predictable and plannable maintenance of the components during planned downtimes.

With aivis, watching critical components is so simple, that it can easily be scaled up.

Example

Watching proportional valves

Proportional hydraulic valves are sensitive for unexpected maintenance which causes unplanned downtimes and lowers productivity. However, an anomaly detection model watching the valves can significantly lower these downtimes. Because, luckily, only in the rarest of cases does the valve fail from one moment to the next. Usually, impending failure looms hours or even days before the actual breakdown enabling timely maintenance during planned downtimes and maintenance windows.

For creating the anomaly detection model, to watch a valve, aivis only requires the target-actual comparison signal. It then automatically searches all available signals of the valve for critical relationships that make the unhealthy behavior of the valve visible. The model can be easily deployed into the components live data streams.

In general, aivis can do the same for any critical components or system that provides enough data.

Creating soft sensors for process quantities

In paper production, not every process parameter can just be measured. Some parameters are very difficult or even impossible to measure directly, such as e.g. the paper quality.

As a result, operators have to derive these variables from their experience and other data. But as processes get more complex and data volumes more extensive, this becomes more and more challenging. So operators sometimes have to literally fly blind, which lowers productivity and can even be dangerous.

This is where soft sensors can make a huge difference. These sensors use a mathematical model to deduce the critical process parameters from more accessible process parameters. The model is gained by analyzing and learning from the relations of the target signal to the other signals in the historical data.

As soft sensors have been known and used for several decades now, their creation has always been a struggle. Data scientists and process engineers had to work hand in hand to work out critical relations between the signals and cast them into a model. Furthermore, the soft sensors required continuous maintenance since their models tended to drift and had to be realigned.

This changes with aivis: You just have to define your target parameter and press start. aivis then figures out all relevant relationships on its own, quickly creating an accurate and durable soft sensor.

With aivis, creating soft sensors becomes fast and simple.

Example

Measuring paper quality

Although paper quality is a critical process parameter, a direct and online quality measurement with a sensor is impossible. Instead, a lab measurement is required, which takes about one hour from sampling to the result. In the meantime, the operator is flying blind, so to speak.

aivis can create a virtual sensor (soft sensor) for the paper quality very quickly. As a result, the live quality prediction enables the operator to continuously and instantaneously adjust the process, which lowers necessary safety margins, significantly reduces material consumption, and increases throughput.

In general, aivis can do the same for any process parameter than can be derrived by other parameters.

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