Industry 4.0 is not just a profit engine for industrial companies — it’s also a powerhouse when it comes to combating energy and material waste, enabling more sustainable plant operations. Processing corresponded with Paige Marie Morse, Industry Director at asset optimization company AspenTech, to discuss technology’s role in helping the process industries move toward a “greener” future.
Q: What do you consider some of the biggest pain points in industrial and energy waste?
A: When it comes to waste in industrial processes, there are two primary areas of concern: production waste and energy waste. Errors during the production process can lead to waste product, lost raw material and wasted energy. And inefficient and unstable processes often demand more energy than necessary.
The losses can be extreme in some processes when a process variable — such as a fluctuating temperature, equipment issue or operator error — can effectively ruin the entire batch. There are many factors that might contribute to errors in industrial processing, and often, due to unanticipated reasons, it’s quite challenging for any organization to curb waste without deep insight and a holistic view into processes as they are happening in real time.
In the chemical industry, for example, some processes demand high energy input, so ensuring on-spec production is important, as is optimization of energy inputs throughout the process. However, curbing operational inefficiencies and energy waste — like material waste — requires much more than the human eye.
Q: What types of technology are helping to reduce material waste?
A: Material waste can occur when a production process deviates from the expected path — in either batch or continuous processes. Digital technologies can be very helpful in determining the reason for a lower-quality product and defining the path to avoid it in the future. In particular, multi-variate analyses consider the broad mix of factors that occur throughout production to determine which has the most impact for avoiding a problem and ensuring a successful outcome. These solutions have typically been applied in retrospect, after a poor batch has been made, to learn how to avoid another loss in production. These solutions can now be employed on-line, to catch the drift from optimal conditions during a process so corrections can be made early enough to recover the product.
An important digital technology that also applies to waste avoidance is advanced process control. This technology helps to carefully control unit operations of continuous processes, often moving in tiny increments, to ensure that none of the key reactor variables stray from the desired range. This technology also helps to maximize output of the production process, as more stable processes can often run closer to their production limits, reducing energy input per unit of product.
In polymer processes, digital production sequencing is a critical tool to avoid product waste. By using a digital solution to schedule the optimum order and timing of each polymer batch, less off-spec material (typically called transition) is produced between batches. It is important to note these sequencing solutions are also used to minimize energy usage by considering temperature differences between production processes for each polymer.
Q: How can advancements in tech reduce energy output?
A: Targeting lower energy consumption (and the commensurate reduced cost) is often the focus for a digitalization project, and many chemical companies have started their digital transformation efforts on these types of projects. This effort can begin at the early stage of process development, when alternate process routes can be compared for energy consumption or carbon dioxide and other emissions using digital simulations. During production operations, this simulation can again be used to optimize energy use during production, including optimization of utility selection. During operations, these simulations are typically referred to as a “digital twin” since it is a digital representation of the actual process.
Other technologies can help reduce energy output at the very early stages of production. Supply chain scheduling technologies, powered by advanced sequence optimization and machine learning, enable organizations to have more guidance on what products to make and when. This optimizes scheduling and improves overall quality of production. It also limits energy output, configuring processes that maximize utilization of existing assets in the most efficient ways possible, and reduces factors like too many process transitions that use an overwhelming amount of energy.
A good example of process control success involves AspenTech customer Braskem. The Brazilian chemical company witnessed a 20% reduction in energy used per ton of ethylene. Another AspenTech customer, LG chemicals, a large chemical producer in Asia, used process models to reduce overall energy use by 10% and reduce its carbon footprint.
Ultimately, more efficiency means more energy savings. Another example of how technology is leading to energy reduction is through artificial intelligence (AI) and machine learning. Advanced AI identifies problem areas that could lead to downtime in the future. This is predictive maintenance, and its value-add is that it foresees process or equipment issues long before they become actual problems — sometimes weeks in advance. It also provides prescriptive action for organizations to take. Oftentimes, when equipment is malfunctioning, it’s also creating inefficiencies that lead to more energy expenditure. By thwarting inefficiencies through tech, organizations will effectively use less energy. With a bulk of industrial companies’ operating costs related to energy, new tools that create value in finding new efficiencies always impact the energy usage bottom line.
Q: What’s next for technology in improving sustainable industrial environments? What’s on the horizon in the next ten years?
A: An emerging performance demand for digital technologies is how they can be most-effectively applied to develop the circular economy. A renewed focus on resource efficiency and reducing the new material input required for production is taking center stage for many process companies. Digital technologies are important enabling solutions in this development — digital simulations help companies develop new routes to de-polymerize plastics or develop alternate bio-based raw materials for the desired product. Additionally, digital supply chain solutions can help companies better track the raw material inputs to their processes, and the resulting disposal after use so recovery for recycling is possible. And as suggested by CEFIC (European Chemical Industry Council) in a recent report1, AI can help lifecycle analysis of products by helping companies assign and track ratings regarding the three dimensions of sustainability.
Paige Marie Morse is the Industry Marketing Director for Chemicals at Aspen Technology. She has significant experience with leading operating companies — including Shell, Dow, Sunoco and Clariant — in R&D, marketing, commercial and strategy roles. Dr. Morse has a BA in chemistry from Kenyon College and a PhD in chemistry from the University of Illinois.
Molecular Managers: A journey into the future of Europe with the European Chemical Industry, CEFIC, June 2019