Processing, December 2020

mainly focusing on energy efficiency but companies are aware that energy efficiency is only a portion of the challenge with the demands of the circular economy and energy transition also necessary Digital technologies provide a means to rapidly accelerate current efforts and important for any business activity chart progress and show success Chemical processors are beginning to explore and invest in alternative energy sources and are preparing to meet the unique demands of the circular economy where economic activity is decoupled from the consumption of finite resources and waste is re integrated into processes This is leading to a fundamental restructuring of current business models Addressing the demands of the circular economy and the energy transition require longer term efforts as companies develop new product and process alternatives that have less impact on the environment For example net zero targets for 2050 will probably require that carbon capture processes be further developed and optimized Digital simulations are already aiding these processes although more optimization remains to be done Additionally digital technologies are helping companies develop more efficient processes to produce hydrogen ammonia and biofuels as energy alternatives And new processes for key chemical processes are needed to lower energy consumption and avoid waste Q How can industrial AI specifically hybrid models lend itself to supporting and scaling sustainability efforts for organizations Artificial intelligence AI offers transformational capabilities to industrial operations offering new levels of operational efficiencies by combining the insight of domain expertise with existing asset experience Industrial AI is the term used to describe this application to industrial challenges strengthened by AspenTechs four decades of domain expertise in chemicals and energy The AI capabilities act as the enablers for more autonomous intelligent processes and the embedded engineering fundamentals act as the infrastructure for safe and efficient operations Embedding AI in process models helps companies develop more efficient production options that use less energy and resources enabling easy comparison of process options Deeplearning advanced process control APC helps companies apply the optimizing power of APC to more processes expanding production efficiencies while also boosting throughput And in context guidance provided by AI enabled insight from previous operations supports less experienced users as they expand digital applications to drive further improvements Aspen Hybrid Models are at the center of these capabilities which combine AI with first principle model design and domain expertise Engineers can now build enriched process models faster using machine learning to leverage simulation or plant data integrating application knowledge including first principles and engineering constraints without requiring deep process or AI expertise These hybrid models can be used to optimize operations create soft sensors such as color or viscosity design new equipment or integrate asset wide processes such as crude to chemicals For all these applications better understanding how process conditions influence product quality can help engineers predict emissions and reduce waste insight that can help companies progress to achieving sustainability targets Aspen Technology www aspentech com DECEMBER 2020 www processingmagazine com 11 A