Today companies in the industrial space have started to place increasing emphasis on improving sustainability performance. Apart from industries increasingly addressing government regulations and expectations from their executive boards to adjust to more environmentally-friendly operations, many of their workers, especially in the Millennial generation, feel strongly about greener production in every aspect of their lives: in the home, travel, and at work. “Green” is a movement.
Many companies are now critically focused on the fragile balance of sustainability goals, equally considering people, planet and profit. Industry 4.0 technology is changing the game, dismantling the long-time status quo providing the insight and guidance to boost sustainability performance, especially at the production level.
The truth is that digital tools have been helping businesses with sustainability performance for decades, mainly focusing on energy efficiency, pollution control and value chain optimization. Renewed efforts are now targeted across business and value chains and require better visibility and insight to target success.
Many organizations are already using a variety of digital tools to reduce their energy consumption or possible waste from production. However, businesses have typically measured success in dollars or other local currency. The measure of success is changing today from financial measurements to more specific emission or efficiency metrics that modern technology can provide.
With the countless innovations in the IoT that exist today, it is important for companies to find a solution that will provide insight not only into carbon dioxide (CO2) emissions but also the specific emissions or wastes that are associated with different process options. For example, companies are asking for CO2 metrics on their operational dashboards to better determine process emission impacts, or look for water use efficiency in production. Operators need to find a solution that will provide these measurements.
Advanced emission tracking technology directly measures output, tracking and then correlating those emissions to specific processes, enabling organizations to adjust operational activities or process design. For example, in very complex processes with multiple steps, it is difficult to see the entire emissions profile, so, if tasked with curbing CO2 it would be difficult to pinpoint which processes are the largest contributors.
If you cannot see it, it is impossible to adjust for it. Tracking solutions give scope of the entire process, providing a view into the emissions of NOx, SOx, and many other pollutants. This insight helps engineers and operators understand how their activities impact environmental metrics.
But emissions tracking is only part of energy management equation. Process simulation and modeling tools can help product developers discover and optimize new processes that inherently produce less waste, while at the same time designing in key safety performance from the start.
These solutions help to improve plant safety and performance, not only reducing energy use and production waste but also maintaining stable operations. It leads to time-saving workflows (an energy-saver) as well as gives plant operators the ability to evaluate plant conditions and many different scenarios within those conditions to help support overall energy and waste management.
Additionally, on-line multi-variate analysis solutions help operators correct potential problems as they occur, so production issues are discovered early and batches can be recovered before they are lost as industrial waste. A good example of this might be the identification of raw material variations as they enter the production process.
Instead of entirely ruining the end-product, the technology will alert operators to these variances so adjustments in the process can deliver quality end-product. Such analysis can also help to correct conditions due to operating anomalies that can lead to off-spec production.
Apart from insight into energy emissions, multi-variate analysis also provides businesses with data-based decisions to help leaders decide what products to make, when to make these products and how to adjust for variable conditions, thus simultaneously improving quality of production and energy efficiency.
Quality production reduces the risk of developing unusable, off-spec products. When a company makes a specialty product, if that batch is made incorrectly, the output is considered waste. Because of this, anything organizations do to improve what they sell to customers automatically creates less waste. So, in a way, profitability is tied to sustainability, and modern technology enables the efficient operations that lead to greater profitability.
Unfortunately, a major roadblock to quality production and a contributor to material waste is unplanned variables. Process manufacturing – because it is often so complex with multiple components – is very susceptible to uncontrolled variables, be it weather or variable raw materials.
It is time-consuming and impossible for people to decipher these irregularities and make appropriate adjustments to account for them. Intelligent technology not only provides insights into variability but also issues guidance for operators and process engineers to take action or make adjustments to save the end product.
Historically, one of the biggest uncontrolled variables in production was equipment breakdown. Artificial intelligence is helping to turn that notion on its head. Through use of both historical data analysis and contextual knowledge of process conditions that lead to different outcomes, AI can integrate that insight to make accurate predictions about asset behavior.
For example, AI and advanced technologies can provide warnings about production, detecting breakdowns weeks or months before they happen. Operators can then plan to idle equipment before a breakdown, eliminating potential hazardous work situations and associated emissions due to the breakdown. Predictive maintenance also aids energy management avoiding losses and possible product loss, both of which will not contribute to the bottom line.
When considering how to approach waste management, be it product-side or energy usage, modern technologies are invaluable to making strides in this area. These technologies add to the capabilities of the efficient, quick-thinking engineer or operator to give the holistic view he or she needs to know how to adjust operations to ensure productive operations. Technology gives the engineer, the operator, a consistent lens through which to make smarter decisions at the all stages of the manufacturing process.