Picking Low-Hanging Fruit with DAQ

Now That We Can Collect All This Data, What Do We Do With It? Turns Out One of the First Places All That Formerly Stranded, Rescued Data Comes into Play Is in Energy Savings and Sustainability

By Nancy Bartels

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Necessity is the mother of invention, goes the old saw. Whether driven by regulations, stockholder pressure, concerns over the price of energy, the desire to be a good corporate citizen or even just seeing a good marketing strategy on the horizon, sustainability initiatives are quickly evolving from being "nice-to-haves" to corporate "must-haves." And one of the easiest jumps onto the sustainability bandwagon is via energy savings. Even the most cynical global warming skeptic can appreciate a five-, six- or even seven-figure saving on energy bills. At that point, whether the practice is "sustainable" or not is irrelevant. It's simply good economics.

One of the bottom line basics of a good energy-saving strategy—as well as process optimization and quality control—is data. You need to know how much you're spending, what you're spending it on, and how well your processes are operating within given parameters. The answers to these questions lie not just in the office of the CFO or the heads of your most senior operators, but in the multitude of data in your PLCs, sensors, SCADA systems, HMIs and all the other basic monitoring systems in your factories and facilities.

The challenge has always been to get that data from the "islands of automation" into the hands of the people who can make decisions about how to manipulate processes in a way to both optimize the processes and systems to reap the savings that can be derived from optimized operation. Fortunately, getting from here to that ideal of real-time information, which enables the decisions that make energy savings (and other process optimization) possible, is getting easier. This is thanks to the happy confluence of improved hardware and sensor technology, connectivity solutions, GUIs and understanding of the implications of the Internet and both its promises and problems for the process industries.

The good news for end users is that most every major automation vendor has jumped on this energy-saving bandwagon to one degree or another, and has offerings that can meet the needs of both the simplest and most complex applications. The other piece of good news is that these kinds of changes in operation can be incremental. It's relatively easy (and inexpensive) to start small and build on little successes. Of course, sometimes, it makes more sense to go big from the beginning.

Steel Driving

Take the case of Tata Steel, the sixth largest steel company in the world with an annual production capacity of 30 million metric tons (tonnes). Tata's Jamshedspur Steel Works in Jamshedpur, India, is the company's home base and its first plant, established in 1907. In 2010-11, it produced approximately 7.5 million tonnes of iron, 6.8 million tonnes of crude steel and 6.7 million tonnes of saleable steel, including rolled and forged bars, hot-rolled coils and strips, cold-rolled coils and semi-finished steel. The Jamsedpur facility is equipped with six coke oven batteries, four sintering plants, seven blast furnaces, five Linz-Donawitz converters, three billet steel shops, two slab steel shops and rolling mills that produce wire rod, bar, plate, and hot and cold strip products. Not surprisingly, an operation of this size consumes a lot of energy and produces a lot of CO2 emissions.

Energy saving and emission reduction has been part of Tata's plan long before either of them became missions du jour for process operations in general. The company has already reduced the amount of energy it needs to make a tonne of steel in half over the last 40 years. Now it has added the goal of cutting CO2 emissions by another 20%. The India division alone has set a target of reducing CO2 emissions from 1.8 tons per ton of liquid steel to 1.5 tons. To get the job done, the company has set up an Energy Management Center with a SCADA system that gathers all the plant site energy information, and manages the load dispatch. Honeywell is the chosen automation partner for the project.

Tata's latest efforts began in 1998 when the company acquired eight PLC-based remote terminal units (RTU). More were added to the network between 2006 and 2010, along with field instrumentation and modifications to the SCADA system. The company also installed a plantwide, dedicated, more than 100-km, fiberoptic network to connect the various shops spread over the facility.

Honeywell's ExperionPKS automation system, the SCADA system and the network have enabled Tata to get accurate measurements and records of energy consumption, correlate energy consumption of process units to their measured output, and set benchmarks using data from the historian. The system also gives plantwide access to the energy supply and consumption of the entire site through a centralized server to enable monitoring of individual energy networks and process units in real time, all of which will enable Tata to meet its emission-lowering goals.

Milk Run

While not as massive an operation as Tata Steel, Murray Goulburn (MG), a cooperative of some 3000 dairy farmers and the largest milk processor in Australia, also uses its sliced-and-diced data to improve its operations and quality of its product and save energy.

Much of Murray Goulburn's product ends up as dried milk powder. Extracting sufficient moisture from whole milk and cream to make powdered milk products may seem like a process as far removed from steelmaking as one can get, but in its own way, the processes are equally delicate and require careful monitoring. In MG's case, collecting that data involves special software from Rockwell Automation to automatically monitor and precisely control the most critical part of the process—the final drying stage.

MG's operation near the village of Koroit in Queensland uses four dryers to process skim and whole milk into a variety of dairy-based powders. The dryers, which stand up to six stories high and are 20 meter in diameters have steel chambers (Figure 1).

Swirling air, which can reach up to 220 ºC, removes the water from droplets until all that remains is a small particle of milk powder not much larger than a dust mote. As the droplets fall, the air cools to about 65 ºC.

One of the crucial variables that affects the quality of the powder is its moisture content. Depending on its end use, the powder should contain between 3% to 6% moisture. The key to achieving the right moisture level is to control the temperatures of the air entering the tower, the static fluid bed and the vibrating fluid beds.

MG operators used to change the temperature setpoints manually to control throughput and moisture content based on their experience and using feedback from moisture samples taken once an hour. But, tests on samples taken every hour showed the moisture on a single product would often vary by as much as 0.3%. Given that overall 3% to 6% range, that's still too much. To create a more consistent product, the temperature setpoints would need to be adjusted automatically based on a predictive model of the dryer.

Maintaining temperature and moisture balance was also important for keeping production at its highest possible level. When the moisture in the air exiting the dryer was too high, the dryer could block and shut down, leading to wasted time, wasted resources and, ultimately, reduced yields.

"We knew we needed an automated system to reduce the moisture variability of the powder," says Geoff Rome, automation and utilities engineer at Murray Goulburn. "Our goal was to find a solution that would help us maintain consistent quality, while increasing final product throughput."

To meet its goals, MG selected the Rockwell Automation Dairy Dryer Solution powered by Rockwell Software's Model Predictive Control (MPC) and Optimization technology. The solution continually collects data from each dryer, and uses predictive models to calculate optimum temperature setpoints for controlling and maintaining the desired moisture level.

Specifically, the system uses Predictive Quality–Soft Sensors to provide in-line, inferential quality measurements and facilitate real-time and frequent control feedback. The system automatically collects data inputs every 15 seconds, resulting in a significant reduction of moisture content variation. Once every hour, a sample of the powder is analyzed with an infrared spectrophotometer to confirm the model. The results are automatically sent back to the modeling system where adjustments are automatically made if necessary. With the new system the moisture variability levels in each dryer were reduced on average by 52%, giving MG  an average of one tonne more of powdered milk product per day across its four dryers.

"Those extra tonnes of powdered milk out the door made the investment in the Rockwell Automation solution well worth it," Rome said. "Our reduction in energy costs has also contributed to the ROI by 5% to 10%."

And the One-Man Band

Similarly, you can't get much smaller than GG Services in Los Angeles. Gavin Gray is the sole owner and employee of this automation systems integrator and machine builder operation, specializing in alternative energy systems and industrial machines.

One of his clients needed remote, real-time access to the Opto 22 control system Gray built for its mobile water treatment system. Gray recognized that delivering this remote access was going to demand a lot of his time, which would mean additional costs for his client. He was up to the job, but Gray had added remote monitoring and HMI capabilities to control systems before and knew that installing, configuring, debugging and deploying the various elements of a remote access system can be complicated.

To do an end-run around many of these issues, Gray went back to Opto22 for its new groov system for building and deploying simple operator interfaces for Windows-based systems. The system enables users to simply and quickly build customized HMI interfaces for either computers or mobile devices to provide easy, remote access to necessary process control data. Best of all, the system costs less than $2000, and can be up and running in under an hour.

After installing a communications link at the mobile water treatment plant, Gray configured the groov Box, and built screens for the interface. On start-up, real-time data on pH, ORP, flow rate, conductivity and other parameters were immediately available.

The client can also remotely controls pumps and filters, set tank levels, and track power use. "With real-time data from the water treatment system," says Gray, "we can see problems and make corrections, as well as get general information."

Rethinking Energy Management

Matthew Littlefield, president and principal analyst at LNS Research, sees sustainability and energy management as part of a bigger picture than just saving dollars on electric bills. He says, "It's going to require a shift in thinking of energy as a budgeted facility cost to thinking of it as something that's an input to production. Energy is then just another raw material that has to be managed."

Energy management beyond just switching light bulbs or installing variable-speed drives is still a nascent field, he adds. "We're still in the ‘Golly, how do we make it work' stage."

However, harvesting stranded data and applying it to an energy-saving program "is a good starting point for companies," he says. "You can integrate it with an operational excellence program. Energy is a piece of that puzzle, and can easily be the low-hanging fruit to get you started.

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