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Computational Fluid Dynamics (CFD) Simulations in Food Processing
Published in Surajbhan Sevda, Anoop Singh, Mathematical and Statistical Applications in Food Engineering, 2020
Abhishek Dutta, Ferruh Erdoğdu, Fabrizio Sarghini
Mathematical modelling approaches are important tools for optimizing and designing food processing operations. The first computational mathematical modelling study in food process engineering was introduced by Texieira et al. (1969), where a finite difference numerical methodology was used to optimize the retention of nutrients in conduction-heated foods. Mathematical modelling in food process engineering relies primarily on physically fundamental mechanisms governing a process which can be helpful in providing a basic definition of the process (Singh and Vijayan, 1998). It might be categorized into two general groups: Experiment-based empirical modelling and physics-based modelling. The first group relies greatly on the availability of the experimental data, and the resulting empirical equation is mostly available under the given range of the experimental conditions. Such an approach is often based on data fitting equations rather than real modelling. The use of the available instruments and lab conditions also affect the generality of these types of models. On the other hand, physics-based models rely on solving the required (partial) differential equations describing the process with valid initial and boundary conditions assumptions. Hence, once the developed mathematical models are (experimentally) validated, their uses for generality like to virtualize the process or for further design and optimization purposes are more adequate. The solution of the partial differential equation set, depending upon system complexity and initial and boundary conditions, might be carried out in three general ways: The first approach, if simplifications can be made, is to use the exact solutions of the differential equation, limited to simplified models on simple or regular geometries.The second approach is based on using numerical methods, like finite difference, finite element or finite volume schemes, in order to solve simplified models based on partial differential equations governing the process with the required initial and boundary conditions. This is typically referred to as the computational fluid dynamics (CFD) approach.
Energy modelling and energy saving strategy analysis of a machine tool during non-cutting status
Published in International Journal of Production Research, 2019
Xiaona Luan, Song Zhang, Jie Chen, Gang Li
This paper presents a non-cutting status power consumption model of a machine tool to characterise relationship between speed and power consumption based on empirical modelling and experiment analysis. Based on the analysis results, some positive energy saving strategies can be adopted. These improved models will provide a reliable prediction of power consumption under various process variables and enable manufacturers to develop potential energy saving strategies during product design and process planning stages. The article is structured as follows: The modelling method for the energy consumption during non-cutting status will be presented in Section 2, including fixed power, spindle idle power, feed motion power and rapid feed power. In Section 3, experiments were designed to study the characteristic and modelling method of the proposed power. Some experiments were used to calculate the coefficients, and the others were applied to verify the accuracy of the proposed model. The experimental results and discussion were included in Section 4. Finally, regression analysis and ANOVA (Analysis of Variance) were applied to illustrate the prediction accuracy of the proposed model. The main conclusions of this study were summarised in Section 5 ‘Conclusions’.
Optimized isolation and characterization of cellulose for extraction of cellulose nanocrystals from Ensete ventricosum pseudo-stem fibre using a two-stage extraction method
Published in Journal of Experimental Nanoscience, 2023
Abnet Mengesha Dube, Bulcha Jifara Daba, Melkiyas Diriba Muleta
An empirical modelling method used in the creation of experimental models and experiment design using response surface methodology (RSM). The surface plotting and statistical analysis were done with Design-Expert 11. The three independent variables were sodium hydroxide concentration (2, 3.5, and 5%), reaction time (120, 180, and 240 min), and reaction temperature (50, 75, and 100 °C). The yield of cellulose was the outcome, and the selection range for each variable was displayed in Table 1.
Modelling of carbon utilisation efficiency and its application in milling parameters optimisation
Published in International Journal of Production Research, 2019
Zhaohui Deng, Lishu Lv, Wenliang Huang, Linlin Wan, Shichun Li
Quantifying the energy consumption and carbon emission in machining process is an important link to achieve sustainable manufacturing. A non-cutting status power consumption model of a machine tool to characterise relationship between speed and power consumption was presented by Luan et al. (2018) based on empirical modelling and experiment analysis. In the same way, a novel mechanistic model is proposed and validated for the consumption of energy in milling processes (Asrai, Newman, and Nassehi 2018). Wang and Choi (2016) incorporated stochastic lot sizing optimisation with two dominant carbon emission reduction mechanisms the carbon emission constraint and the cap-and-trade system to examine their operational and environmental impacts on make-to-order manufacturing. Cao and Li (2014) proposed a simulation approach based on hybrid Petri-nets (HPN) to display the carbon emission dynamics, on the basis of the carbon emissions dynamics described by general state equations of manufacturing system as a hybrid system. Gao et al. (2016) discussed the breakdown of the processes that contribute to the overall carbon emissions of a stamping process chain to meet the needs for low carbon manufacturing. Du et al. (2015) established a system framework of low-carbon operation models in machinery manufacturing industry combining the theory of product life cycle, which consists of an objective layer, a strategy layer, a process layer and a supporting layer. Similar work can be found in Zhang et al. (2016) studied the carbon emission of product assembly process. The results showed that the carbon emission of welding was the largest portion during the assembly processing of air conditioner outdoor. In our previous work, under the perspective of life cycle, a carbon system running model of machine tool parts was presented. Then the carbon emission quantitative model was established based on the material flow, energy flow and environmental emission flow, it can be used to identify and analyse the various stages of carbon emission (Deng et al. 2017). For all those models provided theoretical basis for optimising the machining parameters to achieve low carbon manufacturing.