Artificial Intelligence(AI) is one of the hottest worlds in the industrial world currently. AI refers to the simulation of human intelligence in machines so as to make them perform complex tasks similar to or beyond human capabilities. In recent years it has impacted various industries immensely. Moreover, contrary to popular belief it has been an integral part of chemical industries too for a few decades now.
How it AI made its way into the chemical industry
It would be fair to say those earlier breakthroughs were given by researchers like Gary Powers, Dale Rudd, and Jeff Siirola during the 1970s. Since then large scale exploitation of AI in chemical engineering began. Then came Expert Systems that we’re able to solve complex problems just like humans with expertise in a certain domain. Much was of the work was focused on process design and modeling. However, they lost their charm during the early 90s complying with the neural networks. The advantage that is posed was that a neural network can adapt its criteria to better match the data it analyzes but at a cost of much more computing power and added costs. Despite all these efforts in these 20-30 years, AI failed to deliver to an extent that was expected. Major contributing factors were a lack of powerful computing, storage, programming environments, and less amount of data available.
AI in process modeling
Process Modeling is the most important aspect of chemical engineering. It is used in research, design, optimization, control, and many other plant operations. The traditional ways included solving numerous equations governed by various physical, chemical, and conservation laws. All of this meant a lot of variables, degrees, algebra, differentials, and integrals. Usually, the complexity would make the problems impossible to solve. Moreover, a lot of assumptions and rounding offs just added to the mess.
Then, came to the rescue AI with its technologies like Neural Networks and fuzzy-logic that not only would make the task extremely easy. Moreover, now a lot more factors could be included during modeling thus, making the results more accurate from the industrial point of view.
AI in the optimization of chemical processes
Optimization is the method that seeks to minimize or maximize an objective function. That objective function could be anything, profitability, yield, safety, efficiency, or some variable. Daily, many problems of such type need to be solved. But they are too hard and complex to be solved using gradient approaches owing to the highly non-linear nature of the equations governing them. AI and evolutionary algorithms have helped immensely in making these tasks possible.
AI in fault detection and diagnoses
The Chemical plant faces many malfunctions from time to time including instrument failures, disturbances, and parameter uncertainties. These pose a real threat to the people working and can account for huge financial losses to the plants. Thus, making it an important topic for research. The technology currently in use to tackle this hugely depends on fuzzy logic and neural networks.
What the future holds
The progress of AI in the last decade was tremendous and much of the limitations and challenges have vanished. Implementational, organizational and psychological barriers are largely gone. Moreover, companies have started to accept and even embrace AI. They have been investing a lot to include these in their industries’ work-flow. However, the journey seems to have just been started with still a long way to go and we shall see many new and exciting AI-based technologies in the future.