MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

The hydrodynamics of a gas-solid fluidized bed (FB) is affected by the bubble diameter, which in turn strongly influences the performance of a fluidized bed reactor (FBR). Thus, determining the bubble diameter accurately is of crucial importance in the design and operation of an FBR. Various equations are available for calculating the bubble diameter in an FBR. It has been found in this study that these models show a large variation while predicting the experimentally measured bubble diameters. Accordingly, the present study proposes a new equation for computing the bubble diameter in a fluidized bed. This equation has been developed using an efficient, yet infrequently employed computational intelligence (CI)-based datadriven modelling method termed genetic programming (GP). The prediction and generalization performance of the GP-based equation has been compared with that of a number of currently available equations for computing the bubble diameter in a fluidized bed and the results obtained show a good performance by the newly developed equation.

School of Chemical Engineering

Engineering and Technology

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Current Science

Bubble size prediction in gas-solid fluidizedbeds using genetic programming

The drag coefficient plays a vital role in the modeling of gas-solid flows. Its knowledge is essential for understanding the momentum exchange between the gas and solid phases of a fluidization system, and correctly predicting the related hydrodynamics. There exists a number of models for predicting the magnitude of the drag coefficient. However, their major limitation is that they predict widely differing drag coefficient values over same parameter ranges. The parameter ranges over which models possess a good drag prediction accuracy are also not specified explicitly. Accordingly, the present investigation employs Geldart’s group B particles fluidization data from various studies covering wide ranges of Re and εs to propose a new unified drag coefficient model. A novel artificial intelligence based formalism namely genetic programming (GP) has been used to obtain this model. It is developed using the pressure drop approach, and its performance has been assessed rigorously for predicting the bed height, pressure drop, and solid volume fraction at different magnitudes of Reynolds number, by simulating a 3D bubbling fluidized bed. The new drag model has been found to possess better prediction accuracy and applicability over a much wider range of Re and εs than a number of existing models. Owing to the superior performance of the new drag model, it has a potential to gainfully replace the existing drag models in predicting the hydrodynamic behavior of fluidized beds.

International Journal of Chemical Reactor Engineering

Genetic Programming based Drag Modelwith Improved Prediction Accuracyfor Fluidization Systems

Background: Personal care and pharmaceuticals products affect the ambient water quality when these materials enter the environment in the form of human and animal excreted metabolites or as effluents from hospitals, pharmacies, and chemical manufacturing facilities. The stated compounds are difficult to capture and pose a serious threat to the aquatic ecosystem, and human health. Photocatalytic Degradation (PCD) is seen to be an attractive and inexpensive method, when compared to the ones such as ozonation, photo-Fenton oxidation, sonolysis, and photolysis for the elimination of pharmaceutical compounds from the wastewater.Methods: Owing to the underlying complex nonlinear physicochemical phenomena, the design and construction of a “first principles” model for predicting the rate constant of the PCD is a timeconsuming and tedious process. To overcome the said difficulty, in this study, the stated mathematical models have been developed-for the first time-using an artificial intelligence (AI) based novel datadriven modeling method, namely, genetic programming (GP). The GP-based model predicting the rate constant of the PCD of pharmaceuticals uses following inputs: time of exposure of pharmaceutical pollutant to the solar radiation, pH of wastewater, concentration degraded during the measured time, and the ratio of concentration degraded to the initial concentration of the sample. In this study, PCD of three pharmaceutical pollutants, namely, Ciprofloxacin, Naproxen, and Paracetamol was studied and modeled using a single GP-based model. In the model’s input space, the three pharmaceutical pollutants were differentiated using a number of attributes related to their molecular structures.Result: The GP-based model predicting the rate constant of the PCD, proposed in this study, exhibits an excellent prediction accuracy [correlation coefficient (CC) > 0.9] and generalization performance.Conclusion The GP-based modeling strategy introduced here can be fruitfully used in the development of models for a variety of other pharmaceutical degradation reactions.