Air pollution is a major issue all over the world because of its impacts on the environment and human beings. The present review discussed the sources and impacts of pollutants on environmental and human health and the current research status on environmental pollution forecasting techniques in detail; this study presents a detailed discussion of the Artificial Intelligence methodologies and Machine learning (ML) algorithms used in environmental pollution forecasting and early-warning systems; moreover, the present work emphasizes more on Artificial Intelligence techniques (particularly Hybrid models) used for forecasting various major pollutants (e.g., PM_2.5, PM₁₀, O₃, CO, SO₂, NO₂, CO₂) in detail; moreover, focus is given to AI and ML techniques in predicting chronic airway diseases and the prediction of climate changes and heat waves. The hybrid model has better performance than single AI models and it has greater accuracy in prediction and warning systems. The performance evaluation error indexes like R², RMSE, MAE and MAPE were highlighted in this study based on the performance of various AI models.

One of the major sources of petroleum products is obtained from the sea (offshore and onshore). Here the major problem faced by the worker is, maintaining the constant pressure and flow till the extreme ends. In this paper, the parameters such as pressure and flow are maintained constantly by implementing control valves depending on the different pressure and flow rate of the transmitting pipe. PLC (Programmable Logical Controller) is used to automatically regulate the flow and pressure during petroleum product transportation by controlling the percentage of opening of the control valves and pumps respectively. The required set point for pressure and flow rate are obtained by implementing a suitable controller that regulates in a long transmitting concrete pipes. For this, a PLC based PID controller is developed and its open loop responses are identified. The simulation studies are carried out in the MATLAB/SIMULINK platform to ensure the performance of the controller. The controller tuning is done by ZN (Ziegler and Nicholas) PID and Simple-IMC (Internal Model Control) PID, Shams tuning IMC-PID controller. The simulation result provides better control action when Shams IMC-PID controller is used. Shams IMC-PID controller is experimentally verified on the lab scale-setup and the results prove that it provides most consistent performance as compared to ZN and Simple-IMC PID controllers

Pulse width modulation for Selective Harmonics Elimination (SHE) is mostly employed in the reduction of lower order harmonics. The PV system in this research provides input voltage to the reduced switch 31-level inverter, which is based on the Artificial Bee Colony algorithm. With a high gain DC-DC single-ended primary-inductor converter (SEPIC), the PV panel output voltage is kept constant. The Grey wolf optimization algorithm (GWO) approach is used to get the most power out PV scheme. Multi Carrier modulation, a high-frequency modulation technology, is also used in this novel design of the inverter to reduce upper order harmonics. The suggested Artificial Bee Colony (ABC) algorithm, harmonics is compared to a SHE technique based on a genetic algorithm. The hardware findings were confirmed using DSPIC30F2010 controller simulation, and the recommended system was validated using Matlab simulation.