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      Forecasting
      • 18 Sep 2024
      • 3 Minutes to read
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      Forecasting

      • Updated on 18 Sep 2024
      • 3 Minutes to read
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      Article summary

      What is a Forecasting Model and when should I use one?

      A forecasting model uses historical data to predict future events or conditions. By analyzing past patterns and trends, these models estimate future values, enabling businesses and industrial operations to anticipate changes and make informed decisions.

      For example, if a control valve is open 47%, a forecasting model can predict that the flow of the material in the pipe should be 23 GPM based on its historical data. Simple right? This could’ve easily been done using a function generator and enough points on the curve. But how accurate can it be with just one input being fed in? This is where forecasting models reign supreme. Rather than just characterizing from a single source of data, forecasting models make it easy to correlate between multiple data streams as well as taking historical depth into account. So now you can feed in upstream pressure, temperature, density, and any other relevant data source you have access to in order to create a much more accurate predicted value. When trained and validated properly, this predicted value can be used to temporarily take this place of the flow meter in the event of required maintenance, or give the technician a heads up that the predicted value is not matching up with reality, possibly catching a maintenance event before it ever happens. Below are a few other scenarios where a forecasting model can be beneficial when integrated with a plant's control system.

      Predicting Process Values with a Soft Sensor

      Purpose: To estimate critical process values (e.g., flow rate, temperature) when physical sensors or transmitters fail, ensuring uninterrupted monitoring and control.

      How It Helps:

      • Model Application: A forecasting model, trained on historical data of process variables and operational conditions, predicts the values of critical parameters such as flow rate or temperature based on other available measurements.

      • Integration: In the event of a hardware or transmitter failure, the control system uses the predictions from the soft sensor to continue monitoring and adjusting process conditions as if the actual sensor were operational.

      Example: A flow meter might need to be taken out of service for maintenance, during which, a forecasting model that has been trained on all process variables and valve positions around the flow meter is able to accurately predict the flow and allow operations to continue efficiently and safely.

      Quality Control and Early Lab Results

      Purpose: Give an early indicator when the process is going off spec. Sometimes lab results can take minutes, hours, or even days to see if the product is going to meet standards. This typically results in a significant loss of time and material.

      How It Helps:

      • Model Application: A forecasting model is trained on years of lab data and process conditions to give a real time estimation of quality.

      • Integration: The control system adjusts process parameters or performs real-time quality checks based on these predictions to minimize defects and maintain product quality.

      Example: A facility that typically waits 2 hours to get results back from the lab is able to correct a temperature controller that was left in manual. The forecasting model gave them an early indication alarm when it predicted poor quality of product hours in advance.

      Production Demand Forecasting

      Purpose: To optimize production schedules based on anticipated demand.

      How It Helps:

      • Model Application: Forecasting models analyze historical production data, sales trends, and market conditions to predict future demand for products.

      • Integration: The control system adjusts production schedules, inventory levels, and resource allocation based on these forecasts to ensure that production meets demand without overproducing or underproducing.

      Example: Forecasting an increase in demand for a particular product allows the control system to ramp up production in advance, ensuring that inventory levels align with market demand.

      Energy Consumption Management

      Purpose: To optimize energy usage and reduce costs.

      How It Helps:

      • Model Application: A forecasting model analyzes historical energy consumption patterns, production schedules, and external factors (e.g., weather conditions) to predict future energy needs.

      • Integration: The control system adjusts energy usage and operational settings to align with forecasted energy requirements, leading to cost savings and efficient energy use.

      Example: Predicting higher energy consumption during peak production periods enables the control system to optimize energy usage or schedule production during off-peak hours to reduce energy costs.

      Conclusion

      In summary, forecasting models help industrial control systems by providing valuable insights into future conditions, enabling proactive management and optimization. By integrating these models into control systems, plants can enhance maintenance strategies, optimize production and energy use, maintain quality, and streamline supply chain operations, ultimately leading to increased efficiency and reduced costs.

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