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Optimizing Biomanufacturing in Real Time

One of the inherent challenges in top-down scheduling systems, such as manufacturing plans generated from Enterprise Resource Planning (ERP) systems (Level 4 in ANSI/ISA-95 model), are that they are typically based on the ideal state of manufacturers’ processes. The real state may quite different and include issues with equipment, processing steps that didn’t go as planned, and other less-than-ideal occurrences.

For biomanufacturers in the Life Science industry, developing and executing plans based on the real state of the process can maximize production and identify improvement opportunities. Having models of the process integrated with conditions, events, and other real-time information coming from the control systems (Level 2), manufacturing execution systems (MES – Level 3), and other software applications used in the production process such as laboratory information management systems (LIMS) and computerized maintenance management systems (CMMS) enables feasible schedules to be executed.

Recently, Emerson acquired Bioproduction Group (Bio-G), a leader in simulation, modeling, and scheduling software for biomanufacturers. I caught up with Bio-G’s leader David Zhang to learn more about these software applications and how they help improve manufacturing performance.

Bio-G Real-Time Modeling SystemThe Bio-G Real-Time Modeling System software is designed to help biomanufacturers understand complexity, accommodate variability, find bottlenecks, maximize production and understand the implications of any change in the manufacturing process.

It links to real-time data and databases such as SAP, DeltaV DCS, Syncade MES, PI, CMMS, LIMS and other SQL-based data sources. These links are bi-directional providing automatic updating of key process parameters for a robust and comprehensive information model. Continue Reading

Increase Resource Efficiency with Universal, Integrated Metric

If you’re in the energy business—upstream, midstream, downstream, and electrical power generation & distribution, IHS Markit has a great CERAWeek On-Demand Video site of presentations and interviews from the conference.

One example is the presentation, Decarbonization vs. Profit: Eliminating the Rivalry Through Exergy, given by Emerson’s Ana Gonzalez Hernandez. Ana received her PhD from Cambridge University and is a Resource Efficiency & Commercialization Manager, based in the U.K.

Ana sees big potential for manufacturers along a decarbonization path to use a resource efficiency benchmark for continuous improvement. The challenge is to overcome current perceptions which consider energy efficiency, measured in terms of energy intensity, or material efficiency in terms of yield rates or materials intensity, separately instead of holistically. The interactions between energy and materials is much more complicated as they flow through the production process.

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Remote Proof Testing Safety Level Switches

One of the necessities in managing safety instrumented systems throughout the IEC 61511 safety lifecycle is to perform periodic proof tests on the components in a safety instrumented function to verify proper function.

A short 2:44 YouTube video, How to Remotely Proof-Test the Rosemount 2140:SIS shows how this proof testing can be performed remotely for safety level switches to save time & costs and reduce risk. Continue Reading

Applying Embedded Model Predictive Control

The history of model predictive control (MPC) dates back to the early 1970s invented at Shell Oil and was known as Dynamic Matrix Control. MPC was designed at that time to solve largescale control challenges. As technology advanced, this technology could be more widely applied on smaller-scale challenges.

MPC Control on a Lime Kiln ProcessAt this past 2019 AIChE Spring Meeting, Emerson’s James Beall presented Unique Applications for Embedded Model Predictive Control Technology. In his presentation, he shared several examples of smaller applications ideally suited for MPC-based control strategies.

James opened by defining what MPC is. It uses the past to predict the future by using modeled relationships among the process inputs and outputs. It is multi-variable in the numbers of inputs and outputs. These variables can be dependent on and independent of one another as he showed in this Lime Kiln process example.

As explained on the MPC Wikipedia page:

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Improved Machine Control with IIoT

In several Industrial Internet of Things (IIoT)-related posts here on the blog, we discussed a number of ways these technologies can help improve performance in safety, reliability, efficiency & emissions, and production for process manufacturers. But what about hybrid and discrete manufacturers?

In a Control Engineering article, IIoT-ready technologies improve machine controls, Emerson’s Steven Fales describes how diagnostics and prognostics embedded in IIoT devices enables improved business performance.

Control Engineering: IIoT-ready technologies improve machine controlsSteven opens contrasting the typical approach for an operator to troubleshoot on a bottling line by accessing a programmable logic controller (PLC). In an IIoT-enabled bottling line:

…the operator pulls out a smartphone, connects to the machine’s pneumatic valve system, and pulls up a web page showing diagnostic data on the equipment’s pneumatic system performance. It’s apparent a solenoid coil has burned out in a directional control valve that controls one of the machine’s actuators. Within minutes, a maintenance technician plugs in a new control valve and the bottling line is running again with little lost productivity or major control system intervention.

The promise of IIoT technologies is:

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