It used to be a long, twisting trip from operations on the plant floor or out in the field back to the administrative and enterprise levels. All kinds of added devices, programming, signal conversion, networking and other checkpoints were navigated to get data from where it was generated to where decisions could be made, and these snags stifled many efforts to integrate plant-floor and enterprise levels.
Well, many of those avenues and hurdles are getting shorter thanks to more capable components, software and networks. In fact, the tools have achieved so many gains recently that the biggest obstacles may be in the minds of potential users, who mistakenly believe the production-to-enterprise trip remains longer and more difficult than it really is.
For instance, when specialty vegetable fats manufacturer AarhusKarlshamn Sweden AB migrated from manual reporting to 800xA process control with Smart Clients software from ABB, it also tied in its business system. Together, they automatically feed production orders into AAK's hydration plant, and report on which and how much of each material is used. The hydration plant is where the fats’ melting points are analyzed and determined according to each intended application, such as substituting for butterfat or cocoa butter (Figure 1).
"Integrating our control and business systems more closely created an unbroken data chain from customer order through production planning and process control," says Anders Petersson, lead automation engineer at AAK. "This increased productivity allows more secure production, provides valuable feedback that optimizes our raw material purchasing, and simplifies life for our operators."
In addition, Smart Clients lets AAK collect different types of production data in its office, where staff can follow up on key values such as energy consumption, or use asset monitoring devices to signal when equipment service is needed. "We're also using batch control with specific parameters for each batch," adds Daniel Knutsson, automation engineer at AAK. "Each batch is fully traceable, so we can see exactly when it was processed. It's also much easier for our developers to change an existing recipe, or create a new one without disrupting production."
In another move from manual, Debswana Diamond Mining Co., Ltd. in Botswana needed to replace its old, standalone DAS server and emailed reports at its Orapa, Letlhakane and Damtshaa mine's (OLDM) Plant No. 1 with a more dynamic data mining and reporting system. This solution needed to collect real-time, ISA95-compliant data about production and performance, and deliver it to all areas of Debswana's business to improve optimization and resource allocation.
To that end, Debswana recruited South Africa-based Bytes System Integration to implement an ArchestrA system platform at OLDM, including a Galaxy data repository, application object server (AOS), and Wonderware Information Server (WIS) and Historian from Schneider Electric. Plant equipment data is published for analysis in nine WIS reports, and a daily dashboard outlines key information, such as carats and ore tonnages on one screen for all WIS users on Debswana's intranet network.
“One of the most compelling aspects of the Plant No. 1 project was moving from manual information capture to automated data retrieval," says Zwikamu Dubani, IT analyst at Debswana. "This not only greatly reduces errors, but also speeds up the delivery of accurate information. Now, I no longer have to worry about ‘death by spreadsheet.’ ”
Fewer steps saves steam
Likewise, Denka Singapore Ltd.'s polystyrene resins plant on Singapore's Jurong Island buys steam from a local utility, and operates hundreds of steam traps that can fail over time, wasting steam, causing erosion/corrosion and reducing heat-transfer efficiency. The chemical company usually does periodic surveys, and hires contractors to inspect the traps annually, which means accepting some steam loss between inspections. However, it recently added Rosemount 708 wireless acoustic transmitters to 149 of its critical traps, and also began using monthly, subscription-based Remote Monitoring Service from Emerson Automation Solutions.
The transmitters monitor noise and temperature of the steam traps in real time, and transfer data via edge gateways and a wireless 3G network to a Microsoft Azure virtual cloud server. SteamLogic analytic software analyzes the data and generates alerts; experts at Emerson's PlantWeb Center of Excellence review them and report back to Denka; and Denka’s maintenance staff repairs or replaces failed steam traps according to the reports, using new standard operating procedures (SOPs), which saves steam and makes periodic and annual inspections unnecessary.
“The exception reports provided by Emerson become work orders for the maintenance team, and enabled us to reduce steam consumption by 7%,” says Ng Hock Cheong, maintenance manager at Denka Singapore. Besides saving on steam, remote and continuous monitoring at Denka reduces its traps in bypass mode to less than 4%, and identified 15% blow-through and 8% cold units on startup.
"Users want to connect to the field in different ways, and that means expanding beyond the usual DCS to enterprise areas, especially for measurements that aren't core to control, but can help plant performance and reliability," says Moazzam Shamsi, director of global solutions architects at Emerson. "The major oil and pharmaceutical manufacturers are working to understand how to move data into broader architectures. Not everything needs to go through the control system, so they're routing data around the edge of their DCS to new and existing applications."
Shamsi adds that PlantWeb Insight runs on Layers 3 and 4 of the seven-layer Purdue control hierarchy model, which is outside of Layer 2 where its distributed control system (DCS) operates, though they're all part of the PlantWeb Digital Ecosystem. "This also makes adjustments easier because, if a user wants to add measurement points for a pump on a heat exchanger, they'd traditionally have to add I/O they may not have space for, and go to the vendor to add algorithms to the DCS for those new functions. Picking up applications outside the DCS avoids this because, while the DCS keeps plant performance on target, outside data can help manage the business without being directly attributable to the DCS or its requirements. Also, where users previously had to buy a whole DCS infrastructure, they can now pay per-tag for service and scale up as needed, which changes the whole process control business model."