As computing power and speed accelerated over the years, developers, suppliers and users have faced rapidly shifting environments, costs and decisions about where to do their data processing. Practically every device has a microprocessor, so should we do more calculations in the field? Server-based storage appears to be infinite, so should we send all our data to the cloud? The right answer might seem obvious, but in these fast-changing times, unforeseen problems, new networking expenses, and more attractive options can pop up quickly.
“With advances in computing power, low-cost data storage and gains in IT/OT integration, edge-t-cloud, real-time processing is enabling more data-driven decision making than ever before,” says Sanjay Kumar, senior principal for Operations X and Insights Hub solutions at Siemens Digital Industries. “This is why many manufacturers want to bring more real-time processing to the field, where they can use Siemens Insights Hub and Industrial Edge suite of configurable, low-code applications that are scalable, and provide comprehensive analytics and continuous insights. This enables flexibility in processing large volumes of data at the edge and aggregating real-time data in the cloud to enable operators, line supervisors, quality managers, and plant, regional and enterprise leaders to drive improvements at the equipment, line, process, plant and enterprise levels.”
For instance, Chengdu Xihui Water Environmental Co., Ltd. operates seven wastewater treatment facilities, including the plant in Tuanjie Town, China, between Shanghai and Nanjing. This facility has a daily capacity of up to 190,000 tons of wastewater, but like many similar plants, it's also burdened by isolated equipment and process segments, and many manual procedures.
To improve its performance and regulatory compliance, Chengdu decided to digitalize the Tuanjie plant with IIoT technology, namely Insights Hub from Siemens. This allowed the utility to connect almost all of its critical assets and processes throughout its plants, optimize operational transparency and costs such as maintenance, energy and chemicals, while ensuring water output quality.
Move beyond manual
Chengdu used to perform mostly manual tasks to gather water quality parameters, such as concentration of dissolved oxygen (DO), quantity of water intake, quantity of aeration, dosage of chemicals and the reflux ratio. DO and oxidation reduction potential (ORP) sensors measured DO concentration, and uploaded results from the plant’s PLCs to its SCADA system. After that, adjustments were made, but Chengdu reports its lengthy manual processes made it difficult to stabilize water quality.
Likewise, Chengdu’s adds its top three operational costs are machine and parts maintenance, energy consumption and chemicals. However, without measurement and process transparency, staff were forced to perform excessive treatments and overuse chemicals to achieve regulatory compliance. This inefficiency increases machine use, maintenance and energy consumption, as well as unplanned downtime.
To deploy Insights Hub and access its isolated assets, Chengdu installed MindConnect 2040 embedded, industrial PC to connect PLCs to its equipment and processes, and acquire real-time data. The utility also used Insights Hub applications on Siemens’ Xcelerator business platform, and set up IIoT Asset Manager data models to define data collection points, apply tags, and manage assets. These models use Insights Hub Monitor and Business Intelligence applications to view processes and equipment via the cloud, and perform remote monitoring by self-defining alarm rules and responses. The utility also uses Insights Hub Monitor to check any device’s operating data, send notifications to suppliers via real-time KPI alerts, and request remote troubleshooting.
“With Insights Hub, we can measure, analyze and optimize our process and operation costs,” says Jiang YuLiang, digital transformation director at Chengdu. “We can clearly map any bottleneck, and also figure out the relationship between failure operation and cost.”
Analysis cuts electric bill
For example, Chengdu used Insights Hub to analyze blower data, and learned to associate blower blast capacity with present electricity prices. This let the utility control blower output power to take advantage of off-peak electricity prices, and minimize its power costs while maintaining water output quality. In fact, Chengdu reduced its monthly electric bill by 10% and saved 10% on chemicals by refining and digitalizing its chemical administering model.
“Thanks to Insights Hub, our critical assets are well managed,” says Li Junwen, maintenance manager at Chengdu. “We can schedule maintenance ahead of time, and also work closely and efficiently with equipment and service providers.”
In addition, Chengdu reports that advanced analytics and machine learning (ML) aided by Insights Hub has allowed its plant maintenance to shift from reactive or scheduled to a more proactive approach. Now, assets can be serviced based on operating data and actual health, instead of prescheduled, possibly unnecessary, or when they’ve already failed. This further reduces downtime, and lets assets to run consistently with high reliability.
Kumar adds that IIoT and telematics like those Chengdu employs give users an integrated view from the field to the cloud, but generate it faster and more easily than in the past. “All of this is driven by whatever problem users are trying to solve. They need data they can analyze right at the edge, but they also want to distribute their insights to colleagues in plants worldwide. This lets users do comparative analytics, and learn why one plant is performing better or worse than others,” explains Kumar. “Similar data gathering and analyses were done years ago, but they required more time and labor, and it was often difficult to synchronize and structure that data and put it in context. IIoT provides results in real-time that used to take weeks.”