AI is key to improving sustainability through energy management in process industries.
Climate change is driving governments and industries world-over to reduce emissions and achieve net zero as quickly as possible. In this, AI and ML can be game changers. In process industries, hitting KPIs and sustainability targets by manual adjustment alone is impossible. AI-based digital solutions that rely on IoT devices, data analytics, and AI and ML-based applications have much to offer.
Optimizing energy management, for example, contributes heavily to emissions reduction. Data-driven AI solutions manage the optimization, including energy mix planning, consumption and conversion, emission control, and regulatory compliance. In a steel plant with an annual capacity of up to five million tonnes of steel, complex distribution networks for electricity, steam, by-product gases, and imported fuels account for as much as 20% of production costs. Deploying a digital energy management system optimizes production scheduling, throughput, quality, energy-related costs, raw material usage, and emissions.
Further opportunities to reduce emissions are utilizing energy-rich waste gasses that are by-products from metal production to be consumed by other parts of the process or to generate power via captive power plants. This can be fine-tuned by monitoring generation and consumption across plant facilities in real-time. Data is collected from multiple systems to compare allocation with actual consumption, provide real-time demand and supply calculations, balancing, benchmark, and optimal distribution, as well as forecasts based on production plans and historical data modeling. Root cause analysis is applied when a gap occurs between supply and demand. Complementing these management systems with data contextualization and digital twins allows operators to optimize energy consumption.
Fine-tuning productivity through AI in multi-variable processes
Digital solutions can align sustainability goals, and productivity targets for process industries, especially those have high risk to human operators. In processes such as cement manufacturing, plant operators are concerned about deviating from regulatory emission levels regarding harmful gasses. The variables make manual controls challenging, needing operators to work at safe distances, compromising productivity and profitability. Using advanced process control (APC), cement manufacturers constantly tweak the production process to an optimum state, standardizing operations, minimizing variations, and enhancing productivity. APC technology has evolved with new features and embraced the potential of AI. With advanced analytics, AI, and reinforcement learning from the neural network, processes can be further automated so that the performance and accuracy of the models are monitored, and analytics provides new models for the controller. This will reduce engineering time and allow the system to stay at peak performance, maximizing profitability for cement producers while reducing emissions.
The age of industrial IoT is now; AI will be critical in leapfrogging process industries to the next level.
Global pressures such as sustainability, energy efficiency, safety, and transparency are reshaping the ecosystem for heavy industrial processes. While AI and ML are buzzwords today, there is no doubt that adopting AI and ML is the way forward. AI allows engineers to accept a working model that aligns the real world with the digital. Reverse-engineering the realities of industrial operations using AI will enable trust and create competencies needed to realize the future vision for process industries - where integrated digital operations create better outcomes and take people along.