The process industry is constantly evolving as processes are refined and innovative technologies continue to transform the processing landscape. As complex systems are installed, upgraded and monitored, expectations for profitability and smooth delivery of product remain high. Soft sensors with predictive models provide scenarios in which estimations can drive decision-making and improve the reliability of current systems, often working hand-in-hand with their hard-sensor counterparts, creating comprehensive monitoring networks.
Soft sensors are virtual sensors that can alleviate the need for more expensive hardware sensors by their accurate predictions and are utilized heavily in the process industry. Their real-time predictions can alleviate restraints often brought on by limitations: either budget, person hours or current operating equipment. Engineers in the process industry at a chemical plant or a food processing facility may have federal environmental regulations they must contend with. They are in need of an accurate way to measure lab data and temperatures, all the while staying within budget and controlling capital expenditures. They may also have frequent challenges with a part of the process, requiring swift data to troubleshoot and identify the bottleneck and or challenge. Soft sensors can provide an economical and effective alternative to costly hard sensors, which require an expensive investment, require constant servicing and maintenance, and often fail. Through soft sensors, approximated calculations can provide in theory what raw data provides in reality. Using the last year of data, for example, a soft sensor can build a data model, which a process engineer can then use for a variety of calculations and decision-making.
Many physical properties and manual tests performed offline are related to properties, with sensors measured online, from general manufacturing process. For example, the strength of a final product is often related to the temperature the process or the amount of certain chemicals that are added. The strength may only be tested once per hour, but the temperature and chemical usage are measured every second. This relationship allows for soft sensors to estimate the strength in real-time. Another manual lab test may occur once per hour or a few times a day, whereas the soft sensor provides feedback minute-by-minute or second-by-second; soft sensors model “off-line” tests. The soft sensor uses a combination of historical process data recorded from online sensors and laboratory measurements to predict KPIs, replacing manual testing. The greatest benefit to soft sensors in the lab application is faster feedback of changes to physical properties. Simulated testing frees up operators and supervision to work on other high-priority tasks.
Soft sensors maximize your current data and signals that you have already collected in your process. Rather than consistently replacing and spending valuable budget dollars on hardware, the alternative soft-sensor solution works in tandem with your existing hardware sensors. By utilizing what is already there, both complications and downtime can be avoided. Process engineers can use soft sensors connected to your existing hard sensors, and facilities will benefit from real-time analyzing, monitoring and control, providing reliant calculation of parameters where no hardware sensor is available and reducing purchase and maintenance costs.
Through development of the dataPARC’s PARC view visualization software, dataPARC has its own version of soft sensors. Why would dataPARC’s soft sensors be advantageous for you in your plant or facility? Working in tandem with a soft sensor, The PARCmodel component of dataPARC’s product group predicts plant quality variables in real-time, allowing for estimation of properties that are impractical or impossible to measure online.
PARCmodel also reads live data, such as temperatures and pressures from the plant and uses them to calculate estimated quality values from user-entered models. PARCmodel builds models based on first principles or empirical models developed through techniques such as PCA and PLS.
PARCview soft sensors also do the following to ensure smooth operations:
- Utilize Data From Any Source
- Feature Closed-Loop Automation Control
- Are Intuitive and User-Friendly
PARCview provides a familiar user interface for model creation and optimization through drive model building with Trend control.
PARCmodel can read in data from any source available to PARCview, including OPCDA, OPCHDA, Plant historian systems, SQL and Excel.
Leverage your Soft Sensor models for use in advanced-control applications. The Soft Sensor output can be used like any other process value in the control solution.
Do you think that dataPARC’s soft sensors could be helpful in your facility? Contact us today to learn more and get customized product suggestions for your company.