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Energy – exergy analysis and mathematical modeling of cassava starch drying using a hybrid solar dryer ^(Article)

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Department of Chemical Engineering, Faculty of Engineering, Jl. Prof.Soedarto, SH-Tembalang,, Diponegoro University, Semarang, 50271, Indonesia

School of Postgraduate Studies, Jl. Imam Bardjo, SH-Tembalang,, Diponegoro University, Semarang, 50241, Indonesia

Abstract

In this study, we aimed to energetically and exergetically evaluate the usage of a hybrid solar dryer system for cassava drying via a series of drying experiments. The experiments were performed beginning at . A.M. (hereafter, in local time) until the moisture content of cassava starch became constant at a value less than % on a wet basis at drying temperatures of °C to °C and drying times of – min. The results demonstrated that the highest overall dryer energetic efficiency was . %, which was achieved at a drying temperature at °C, and that the maximum energetic efficiency of % was recorded at . A.M. The exergy flows fluctuated during the drying process and were dependent on the solar radiation and drying conditions; however, the exergetic efficiency of the dryer was . %–

. %. Comparison of the fitting models denoted that the Page model was the most suitable model for describing the experimental drying performances. The calculated effective diffusivity constant (D ) and the activation energy (E ) during the drying process from °C to °C were ·× m /s and . kJ/mole, respectively. © , © The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) . license.

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cassava starch energy exergy hybrid solar dryer mathematical modeling

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This work was supported by research grants provided by the Ministry of Research, Technology, and Higher Education of Indonesia for funding this research.

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Open Access Cogent Engineering

Volume 7, Issue 1, 1 January 2020, Article number 1771819

Suherman, S.â  Susanto, E.E.â  Zardani, A.W.â  Dewi, N.H.R.â  Hadiyanto, H.â,b  

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Thin-Layer Drying | High Temperature Air | Solar Dryers

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ISSN: 23311916 Source Type: Journal Original language: English

DOI: 10.1080/23311916.2020.1771819 Document Type: Article

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10/22/2020 Evaluation of hard capsule application from seaweed: Gum Arabic-Kappa carrageenan biocomposite films | Cogent OA

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Evaluation of hard capsule application from seaweed: Gum Arabic- Kappa carrageenan biocomposite lms

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Fatmawati Adam  | Jurida Jamaludin | Siti Hana Abu Bakar | Ruwaida Abdul Rasid | Zulka i Hassan

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Zulka i Hassan is a Professor in Universiti Malaysia Pahang of Chemical and Process Engineering Technology Faculty. His research interests include oil and gas production technology and renewable energy from biomass.

Published: 21 May 2020, https://doi.org/10.1080/23311916.2020.1765682

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Article Figures and tables References Supplementary material

At present, plant-based hard capsule such as hydroxypropyl methylcellulose (HPMC) has a high demand in drug delivery application but the production process is expensive with limited reactant supply. κ-carrageenan has been used as a gelling agent in HPMC hard capsule production. This study aims to develop gum Arabic (GA)-κ-carrageenan biocomposite, a potential material to produce hard Abstract

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(17)

10/22/2020 Evaluation of hard capsule application from seaweed: Gum Arabic-Kappa carrageenan biocomposite films | Cogent OA

Evaluation of hard capsule application from seaweed: Gum Arabic- Kappa carrageenan biocomposite lms

Authors (5)

Fatmawati Adam  | Jurida Jamaludin | Siti Hana Abu Bakar | Ruwaida Abdul Rasid | Zulka i Hassan

About the author(s) 

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Materials Engineering

other 

0 citations

Article Figures and tables References Supplementary material

At present, plant-based hard capsule such as hydroxypropyl methylcellulose (HPMC) has a high demand in drug delivery application but the production process is expensive with limited reactant supply. κ-carrageenan has been used as a gelling agent in HPMC hard capsule production. This study aims to develop gum Arabic (GA)-κ-carrageenan biocomposite, a potential material to produce hard capsule. The GA-κ-carrageenan biocomposite lms were prepared at di erent κ-carrageenan weight ratios of 33% (GC33), 50% (GC50) and 67% (GC67) at constant concentration of polyethylene glycol and alginate. The control lms of GA lm and κ-carrageenan lm were compared. The lm and hard capsule formed from GC67 shows the highest tensile strength and capsule loop of 36.21 MPa and 34.11 N, respectively at 1058 mPa.s solution viscosity at 300 rpm shear rate. The hard capsule disintegrated at 7.30 min. The addition of GA is compatible to make the hard capsule surface smoother. Thus, this biocomposite has the potential to be developed for future hard capsule.

Subjects

Food Additives & Ingredients, Food Chemistry, Materials Science Keywords

seaweed, thermal stability, tensile strength, viscosity, disintegration time Abstract

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(18)

10/22/2020 Multiobjective generation scheduling using multicore processing-based continuous genetic algorithm | Cogent OA

Multiobjective generation scheduling using multicore processing-based continuous genetic algorithm

Authors (2)

Kiran Babu Vakkapatla  | Srinivasa Varma Pinni About the author(s) 

Kiran Babu Vakkapatla received M.Tech degree in electrical power engineering from JNTUH College of Engineering, India in 2012. He has diverse experience in software design and testing in the context of electric power systems. He is currently pursuing doctoral studies at Koneru Lakshmaiah Education Foundation (KLEF), India. The power systems research group at KLEF works actively in the areas of optimization, renewable energy integration and electric vehicles.

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Electrical & Electronic Engineering

Research Article 

0 citations

Algorithms used for day ahead generation scheduling are crucial for a power system operator to balance con icting objectives and the network constraints. Practically feasible algorithms using parallel computing, low-cost hardware and open-source software in power system parlance are rarely attempted in the literature. In this paper, a multicore processing-based genetic algorithm is proposed for nding the optimum solution of economic emission dispatch considering the reliability indices. Continuous genetic algorithm is used to improve the speed of the algorithm. Cost

minimization and emission minimization are considered as the objectives to nd the set of pareto- optimal solutions. The nal solution is selected from the pareto-optimal set based on the reliability of generating stations. The insight used to improve the search space is the usage of two cores of a dual core processor in parallel, with di erent parameters of genetic algorithm. The constraints are

handled using repair function and penalty factors, based on the feasibility of implementation. The algorithm is tested on IEEE 30 Bus, 6 generator system and IEEE 57 Bus system. The results show that the multicore processing using di erent parameters of genetic algorithm has improved the

performance.

Abstract



Article Figures and tables References

(19)

10/22/2020 Multiobjective generation scheduling using multicore processing-based continuous genetic algorithm | Cogent OA

Multiobjective generation scheduling using multicore processing-based continuous genetic algorithm

Authors (2)

Kiran Babu Vakkapatla  | Srinivasa Varma Pinni About the author(s) 



Electrical & Electronic Engineering

Research Article 

0 citations

Algorithms used for day ahead generation scheduling are crucial for a power system operator to balance con icting objectives and the network constraints. Practically feasible algorithms using parallel computing, low-cost hardware and open-source software in power system parlance are rarely attempted in the literature. In this paper, a multicore processing-based genetic algorithm is proposed for nding the optimum solution of economic emission dispatch considering the reliability indices. Continuous genetic algorithm is used to improve the speed of the algorithm. Cost

minimization and emission minimization are considered as the objectives to nd the set of pareto- optimal solutions. The nal solution is selected from the pareto-optimal set based on the reliability of generating stations. The insight used to improve the search space is the usage of two cores of a dual core processor in parallel, with di erent parameters of genetic algorithm. The constraints are

handled using repair function and penalty factors, based on the feasibility of implementation. The algorithm is tested on IEEE 30 Bus, 6 generator system and IEEE 57 Bus system. The results show that the multicore processing using di erent parameters of genetic algorithm has improved the

performance.

Subjects

Energy & Fuels, Electrical & Electronic Engineering, Power Engineering Keywords

generation scheduling, multiobjective optimization, genetic algorithm, power system reliability, parallel computing

Abstract

Article Figures and tables References