Baking of Flat Bread in an Impingement Oven: Modeling and Optimization

Abstract

An artificial neural network (ANN) was developed to model the effect of baking parameters on the quality attributes of flat bread; i.e., crumb temperature, moisture content, surface color change and bread volume increase during baking process. As the hot air impinging jets were employed for baking, the baking control parameters were the jet temperature, the jet velocity, and the time elapsed from the beginning of the baking. The data used in the training of the network were acquired experimentally. In addition, using the data provided by ANN, a multi-objective optimization algorithm was employed to achieve the baking condition that provides the quality of the bread in all aspects simultaneously.

Machine learning‐based modeling in food processing applications: State of the art

“…ML‐based modeling is an exciting option to predict baking kinetics and control the quality of the product without much difficulty. Considering the benefits of ML‐based predictive modeling, recently researchers have attempted to apply ML in many areas of food baking such as the baking of biscuits/cookies (Broyart & Trystram, 2003; Isleroglu & Beyhan, 2020; Olawoye et al., 2020), milk cake (Emerald et al., 2020), bread (Banooni et al., 2009; Sablani et al., 2002), and soft cake (Goyal & Goyal, 2011).…”

Using Neural Network for Determination of Viscosity in Water-TiO2 Nanofluid

“…A few studies have taken into account the effect of temperature on the viscosity of nanofluids. Yang et al [27] measured the viscosity of a nanofluid comprised of graphite nanoparticles at four different temperatures (35,43,50, and 70 • C). They showed that kinematic viscosity is decreased with raising the temperature.…”

“…This tool has been previously utilized in modeling and prediction of Stainless Steel Clad Bead [32], heat exchangers for cooling applications [33], and analysis on performance of heat pumps [34]. Neural network was also used in one earlier work by the authors of this article to predict the quality of bread in cooking process [35]. However, just a few studies have recently adopted this method in their estimations for nanofluids.…”

Heat transfer and flow characteristics of nanofluid in a narrow annulus: Numerical study, modelling and optimisation

“…Time and the volume of calculations can be significantly reduced by the application of this tool in CFD. Neural networks have been used in various fields of engineering . However, just a few studies have recently adopted this method for nanofluids .…”