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RISS 인기검색어
- 검색키워드
- 저자 : Asep Priatna (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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Priatna, Deri,Nandiyanto, Asep Bayu Dani The Institute of Internet 2019 International journal of advanced smart convergenc Vol.8 No.4
We conducted research to evaluate economically and engineering about the synthesis of Magnesium Oxide, MgO, nanoparticles using physicochemical methods. The method used was economic evaluation by calculating GPM, BEP, PBP, and CNPV. The other method used was engineering perspective. MgO nanoparticles were synthesized by reacting Mg(NO<sub>3</sub>)<sub>2</sub> and NaOH with a mole ratio 1: 2. Mg(OH)<sub>2</sub> formed was heated and calcined to remove water content and to oxidation to form MgO. An economic evaluation by calculating GPM and CNPV for the production of MgO nanoparticles on an industrial scale shows that the payback period (PBP) occur in the third year and profits increase each year. Tax variations show that the higher of tax, the lower profits received. When there was an increase of selling prices, the profit was greater. The variable cost used is the price of raw material. When there was an increased in the variable cost price, the payback period was longer and the profits was reduced. The benefit of this research is knowing the industrial production of MgO nanoparticles is beneficial. The function of MgO nanoparticles is a material for the manufacture of ceramics and can be used as an antimicrobial in the water filtration process.
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Deri Priatna,Asep Bayu Dani Nandiyanto 한국인터넷방송통신학회 2019 Journal of Advanced Smart Convergence Vol.8 No.4
We conducted research to evaluate economically and engineering about the synthesis of Magnesium Oxide, MgO, nanoparticles using physicochemical methods. The method used was economic evaluation by calculating GPM, BEP, PBP, and CNPV. The other method used was engineering perspective. MgO nanoparticles were synthesized by reacting Mg(NO3)2 and NaOH with a mole ratio 1: 2. Mg(OH)2 formed was heated and calcined to remove water content and to oxidation to form MgO. An economic evaluation by calculating GPM and CNPV for the production of MgO nanoparticles on an industrial scale shows that the payback period (PBP) occur in the third year and profits increase each year. Tax variations show that the higher of tax, the lower profits received. When there was an increase of selling prices, the profit was greater. The variable cost used is the price of raw material. When there was an increased in the variable cost price, the payback period was longer and the profits was reduced. The benefit of this research is knowing the industrial production of MgO nanoparticles is beneficial. The function of MgO nanoparticles is a material for the manufacture of ceramics and can be used as an antimicrobial in the water filtration process.
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Multi-objective Mathematical Modeling for Scheduling Machines in Parallel with Batch Processors
Evy Segarawati Ampry,Aan Komariah,Dedy Achmad Kurniady,Muhammad Rafiq,Asep Priatna,Muneam Hussein Ali,Haydar Abdulameer Marhoon,Lakshmi Thangavelu,Purnima Chaudhary 대한산업공학회 2022 Industrial Engineeering & Management Systems Vol.21 No.2
In this paper, the problem of scheduling the production of unrelated parallel machines to simultaneously minimize the goals of production time span, early and late fines, and the cost of purchasing machines is investigated, and a two-objective mathematical model is considered considering clearance and preparation times and limit capacity. Due to faster and cheaper operations with batch processors and increasing the efficiency of operating systems, all machines are batch processors. Initially, the model is coded and executed using the exact method in GAMS software. Due to the hard-NP nature and complex nature of the problem, a multi-objective meta-heuristic algorithm based on the coordination search method is proposed and designed. Then Taguchi method is used to find the best level for the algorithm parameters, and two examples of problems in different dimensions of tasks and machines are presented and solved by this proposed algorithm. The results of the calculations show the efficiency of this algorithm to generate more solutions at a much lower solution time.
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