Bioethanol G2: Production Process and Recent Studies

2014 ◽  
pp. 345-364 ◽  
Author(s):  
Yanni Sudiyani ◽  
Kiky Corneliasari Sembiring ◽  
Indri Badria Adilina
Keyword(s):  
Production Process

Optimization of the production process of special purpose pasta with the addition of carrots and pectin

2019 ◽  
Vol 28 (9) ◽  
pp. 50-53
Author(s):  
N.N. Martynov ◽  
◽  
G.A. Sidorenko ◽  
G.B. Zinyukhin ◽  
E.Sh. Maneeva ◽  
...  
Keyword(s):  
Production Process

IMPLEMENTASI JOB ORDER COSTING METHOD DALAM PERHITUNGAN HARGA POKOK PRODUKSI MAP

2018 ◽  
Vol 4 (2) ◽  
pp. 43-55
Author(s):  
Ika Yulianti ◽  
Endah Masrunik ◽  
Anam Miftakhul Huda ◽  
Diana Elvianita
Keyword(s):  
Production Process ◽  
Quantitative Data ◽  
Production Cost ◽  
Qualitative Data ◽  
Cost Data ◽  
Method Calculation ◽  
Cost Of Production ◽  
The Right ◽  
The Cost

This study aims to find a comparison of the calculation of the cost of goods manufactured in the CV. Mitra Setia Blitar uses the company's method and uses the Job Order Costing (JOC) method. The method used in this study is quantitative. The types of data used are quantitative and qualitative. Quantitative data is in the form of map production cost data while qualitative data is in the form of information about map production process. The result of calculating the cost of production of the map between the two methods results in a difference of Rp. 306. Calculation using the company method is more expensive than using the Job Order Costing method. Calculation of cost of goods manufactured using the company method is Rp. 2,205,000, - or Rp. 2,205, - each unit. While using the Job Order Costing (JOC) method is Rp. 1,899,000, - or Rp 1,899, - each unit. So that the right method used in calculating the cost of production is the Job Order Costing (JOC) method

Diagnostics, Optimization and Mathematical Models of Coke-Sinter-Hot Metal Production Process

2019 ◽  
Author(s):  
O. Lebid ◽  
Y. Vyshinskya ◽  
N. Gordon ◽  
V. Chaika ◽  
G. Izumskiy ◽  
...  
Keyword(s):  
Mathematical Models ◽  
Production Process ◽  
Hot Metal ◽  
Metal Production

Image analysis and peanut seeds performance during the production process

Científica ◽  
2016 ◽  
Vol 44 (3) ◽  
pp. 412 ◽  
Author(s):  
Rafael Marani Barbosa ◽  
Bruno Guilherme Torres Licursi Vieira ◽  
Francisco Guilhien Gomes-Junior ◽  
Roberval Daiton Vieira
Keyword(s):  
Image Analysis ◽  
Production Process

PRODUCTION PROCESS OF AN AUTOMOTIVE EXHAUST

2016 ◽  
Vol 4 (2) ◽  
Author(s):  
Tatiane Ribeiro Santos ◽  
Adriele Seabra ◽  
Alison Pereira ◽  
Maria Aparecida Moreira ◽  
Mariane Pereira
Keyword(s):  
Production Process ◽  
Automotive Exhaust

PRODUCTION PROCESS OF AN AUTOMOTIVE EXHAUST

2016 ◽  
Vol 4 (2) ◽  
Author(s):  
Tatiane Ribeiro Santos ◽  
Adriele Seabra ◽  
Alison Pereira ◽  
Maria Aparecida Moreira ◽  
Mariane Pereira
Keyword(s):  
Production Process ◽  
Automotive Exhaust

Formulation of Colors Using a Genetic Algorithm

2012 ◽  
Vol 17 (4) ◽  
pp. 241-244
Author(s):  
Cezary Draus ◽  
Grzegorz Nowak ◽  
Maciej Nowak ◽  
Marcin Tokarski
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Production Process ◽  
Computer Technology ◽  
Manual Method ◽  
Apply Genetic ◽  
Computer Aided ◽  
Multiple Samples ◽  
Plastics Industry

Abstract The possibility to obtain a desired color of the product and to ensure its repeatability in the production process is highly desired in many industries such as printing, automobile, dyeing, textile, cosmetics or plastics industry. So far, most companies have traditionally used the "manual" method, relying on intuition and experience of a colorist. However, the manual preparation of multiple samples and their correction can be very time consuming and expensive. The computer technology has allowed the development of software to support the process of matching colors. Nowadays, formulation of colors is done with appropriate equipment (colorimeters, spectrophotometers, computers) and dedicated software. Computer-aided formulation is much faster and cheaper than manual formulation, because fewer corrective iterations have to be carried out, to achieve the desired result. Moreover, the colors are analyzed with regard to the metamerism, and the best recipe can be chosen, according to the specific criteria (price, quantity, availability). Optimaization problem of color formulation can be solved in many diferent ways. Authors decided to apply genetic algorithms in this domain.

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