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Artificial Intelligence (AI) is one of the most useful technologies during COVID19 pandemic. In this current situation, AI played a vital role in various and different sector from an infected patient to the economy-wide. A wide range of examples are available for how AI tackled with COVID19 and is helping during the pandemic around the world. During this pandemic time, large to small companies have been developing new AI approaches such as droids, machine, software and gadgets in embracing fight against COVID19 pandemic. The present study reviews application of AI and how it has supported in this pandemic.

Safety and economic considerations pose constraints on inputs and outputs of industrial processes. A conventional feedback control system is not equipped to handle these constraints, and hence, industrial practitioners resort to the addition of ad hoc elements such as selector blocks, in conjunction with more sophisticated control strategies such as split range control. However, this approach lacks an organized and prudent methodology and is usually carried out with the aid of experiential knowhow of plant operators and control engineers. Some recent developments in the literature address this issue, however simultaneously alluding to model predictive control (MPC) as a more natural approach for such systems. In this work, we evaluate the efficacy of the aforementioned approach of simple ad hoc additions to accommodate constraints in a process. To this end, with the aid of a continuously stirred tank reactor model, we illustrate the ease of deployment of selector blocks for handling constraints in simple systems and assess their limitations as the system becomes more complex. Next, we consider a heat exchanger employed in a South-East Asian processing facility where the performance of the currently used selector-based architecture is deemed unsatisfactory by the operators. The current control structure was evaluated for conformity with the approaches suggested in the recent literature and was found to be sound; hence, the unsatisfactory performance stems from the inherent limitations of the proportional integral control structure and/or improper tuning. The implementation of MPC on the exchanger system leads to performance amelioration and enhanced robustness.

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