BIG DATA AND IIOT TRANSFORMATIONS IN INDUSTRIAL PLANT OPERATIONS

New digital tools will help operate industrial plants in a much smarter and more efficient way, thus saving their owners huge amounts of money and reducing the negative impact they have on the environment. The more advanced the technological control systems, the closer the transition to zero carbon emissions. Manufacturing has tremendous energy reduction opportunities that are underutilized today. This is due to outdated building management systems and approaches to building operations that are based on a 90s mindset. The greatest potential is in industrial facilities, which are the most energy-intensive and technically complex.

Any optimization starts with the collection and analysis of information, just as any treatment starts with a complete medical examination of a person. The problem with the building operations market is that a building cannot tell you what “hurts” and where the pain is, and the operational tools used today, to continue the medical analogy, are only capable of measuring basic parameters such as pulse and pressure. This is very little. To lead a healthy lifestyle, to diagnose and treat potential diseases at the first sign, you need an ECG, detailed blood tests, and more.

When we talk to our customers, we see the same picture: a lack of understanding of what is happening inside the manufacturing building—how energy is being generated, distributed, how efficient the energy consumption processes and operation of basic utility systems are, and what percentage of energy is being wasted.

The hardest part is older buildings. A huge number of industrial facilities exist from times when ecology and optimization issues were not so acute. As a rule, these buildings are not equipped with platforms for the integration of various utility systems, so owners cannot get even the basic information about the condition of the building.

Unpacking the black box requires working with much more data than is currently available. One of the biggest misconceptions among building owners and operators can be summed up as “why do we need big data; we have a BMS!” Such systems do not provide enough information to meet global environmental goals. They are difficult to connect and integrate, and they become a mix of 90s technologies. Mainly for these reasons, even green-certified office buildings can actually consume more than B- or C-class buildings. Industrial facilities are much more complicated than commercial buildings: they are a complex web of utility systems—some are responsible for production processes, and each factory is unique in that sense, while others supply the building with water, heat, cooling, and fresh air.

Even those who really want to reduce operating costs and follow the ideas of sustainable development cannot do so. It is difficult to optimize something that cannot be analyzed.

Big Data

Our approach to building operations is based on the notion that a building is not just windows, walls, floors, and separate utility systems, but a source of big data that needs to be properly collected, structured, and analyzed.

To realize this concept, our company has developed its own IIoT platform, HiPerWare, with an open interface for importing data from different sources and exporting to other systems (ESG reporting / CAFM / CMMS). It enables us to collect big data quickly and cost-effectively from operational utility systems and then create a digital twin of a property. We collect big data non-invasively, without interfering with the systems, and create a digital twin using a BIM model, a panoramic 360 photo, or a P&ID, creating a “live” digital twin that “feels” everything that is happening online in the real building.

There is a lot of talk today about how a BIM model should work and add value throughout a building’s lifecycle. But without its integration with big data, it is just a static digital building passport—an anatomical cast—with limited applications. But with HiPerWare, it becomes a truly powerful tool for analyzing a building.

This approach differs from the traditional one in that it makes it possible to analyze complex patterns in the operation of various utility systems, find connections, analyze energy consumption, and identify even the smallest technical anomalies that are invisible with traditional tools. 

For example, a recent study of a large manufacturing plant in Germany showed that the workshop was simultaneously cooled and heated. This came as a big surprise to the owners and operators armed with EMS and BMS systems.

This is one of the largest manufacturers in Germany. Despite a strong desire to reduce energy consumption and generally follow the goals of the European Green Deal, the engineers were unable to structure the information they were receiving from multiple sources and understand how energy was being distributed, wasted, and where there were opportunities for optimization.

Using the HiPerWare platform, we began collecting big data and analyzing energy consumption processes, including assessing production energy needs and the efficiency of cooling, heating, and ventilation. From this information, we created an energy model that visualized how efficiently utility systems were operating, how energy was being distributed, and what percentage of energy was being wasted. Optimization opportunities were also visualized, including possibilities for reuse.

And, as mentioned above, an anomaly was found – one of the halls was simultaneously heated and cooled in winter, resulting in excessive consumption of 60 MWh of fossil fuel and 18.5 tons of CO2 emissions per month.

As seen in this example, working with big data and the digital twin provides an opportunity to move from “monitoring parameters” to creating effective operational models, and then to learning. Using artificial intelligence algorithms, digital twins can be taught to detect deviations from the norm and make decisions independently.

New tools can also have an impact on the basics of utility design. Big data is a much more effective source of information than outdated standards that force designers to repeatedly overestimate capacity, thereby increasing consumption.

Turning energy waste into power

The use of IIoT-based tools opens new possibilities not only for finding sources of excessive consumption but also for managing energy “waste”. We’re talking about reusing or converting one type of energy into another, which would allow owners to reuse heat rather than discard it, as almost every facility does today.

Solving this problem would have a huge health impact and help achieve ESG (Environmental, Social, and Governance) goals.

Let’s consider an example of a direct heat recovery ventilation system. It exhausts warm air of about 25°C from the room, and at the same time, the forced ventilation draws in cold air from outside. The two flows pass through a heat exchanger, and the fresh air is heated by the exhaust air. The efficiency of such systems can be as high as 80%.

Energy recycling, on the other hand, focuses on converting waste energy into usable forms. This can be achieved through advanced techniques that convert wasted energy, such as industrial by-products, into valuable energy sources, contributing to a more circular economy.