How to Decide on Modeling Details: Risk and Benefit Assessment

Evaluation of multilayer perceptron neural networks and adaptive neuro-fuzzy inference systems for the mass transfer modeling of Echium amoenum Fisch. & C. A. Mey

BACKGROUND

Multilayer perceptron (MLP) feed-forward artificial neural networks (ANN) and first-order Takagi-Sugeno-type adaptive neuro-fuzzy inference systems (ANFIS) are utilized to model the fluidized bed-drying process of Echium amoenum Fisch. & C. A. Mey. The moisture ratio evolution is calculated based on the drying temperature, airflow velocity and process time. Different ANN topologies are examined by evaluating the number of neurons (3 to 20), the activation functions and the addition of a second hidden layer. Different numbers (2 to 5) and shapes of membership functions are examined for the ANFIS, using the grid partitioning method. The models with the best performance in terms of prediction accuracy, as evaluated by the statistical indices, are compared with the best fit thin-layer model and the available data from the experimental cases of 40 °C, 50 °C and 60 °C temperatures at 0.5, 0.75 and 1 ms^-1 airflow velocity.

RESULTS

The best performed ANFIS model, comprised by 5-2-2 of π-shaped andtriangular membership functions for time, temperature and airflow velocityinputs respectively, was able to describe the moisture ratio evolution of E. amoenum more precisely than the best ANN topology, achieving higher values of coefficientof determination (R² ), root mean square error (RMSE) and sum of squared errors(SSE). The best thin-layer model involving six adjustable parameters, managedto describe experimental data most accurately with R² = 0.9996, RMSE = 0.0057and SSE = 7.3·10^-4 .

CONCLUSION

The results of the comparative study indicate that empirical regression models with increased numbers of adjustable parameters, constitute a simpler and more accurate modeling approach for estimating the moisture ratio of E. amoenum Fisch. & C. A. Mey under fluidized bed drying. © 2021 Society of Chemical Industry.

Electrification of Biotechnology: Status quo

Interfacing microbial, enzymatic, and electrochemical transformations has led to the new field of electrobiotechnology. Among the plethora of applications (including electric energy generation via pollutant removal), the synthesis of chemicals and energy carriers (e.g. H₂) has sparked great interest. The linked transformation of chemical and electric energy may allow the joint utilization of renewable feedstock and sustainable electricity to gain commodities and fuels. The overall field is now referred to as bioelectrosynthesis and is a focus of this book. Starting with the rationale for using bioelectrosynthesis in a bioeconomy, this chapter provides a brief introduction to the field of electrobiotechnology. Subsequently, the chapter discusses the framework for bioelectrosynthesis, which is based on enzymes as well as microorganisms, and provides a definition of bioelectrosynthesis. The chapter concludes with a short overview on the history of the field. Graphical Abstract.