scholarly journals The Holonic Production Unit: an Approach for an Architecture of Embedded Production Process

10.5772/5524 ◽  
2008 ◽  
Author(s):  
Edgar Chacn ◽  
Isabel Besembel ◽  
Dulce M. ◽  
Juan Cardillo
Keyword(s):  
Production Process ◽  
Production Unit

scholarly journals A Look at the Industrial Production of Olefins Based on Naphtha Feed: A Process Study of a Petrochemical Unit

2021 ◽  
Author(s):  
Reza Davarnejad ◽  
Jamal Azizi ◽  
Shaghayegh Bahari
Keyword(s):  
Natural Gas ◽  
Crude Oil ◽  
Production Process ◽  
Industrial Production ◽  
Raw Materials ◽  
Production Unit ◽  
Industrial Scale ◽  
Ethylene Propylene ◽  
Liquid Feed ◽  
Process Study

Olefins (ethylene, propylene and butadiene) as raw materials play an important role in a lot of chemical and polymer products. In industrial scale, there are several techniques from crude oil, natural gas, coal and methanol for the olefins production. Each of these has some advantages. The petrochemicals with liquid feed can simultaneously produce all of the olefins. Shazand Petrochemical Co. (as the first olefins production unit in Iran) produces all of the olefins using naphtha (light and heavy) feed. In this chapter, the production process of olefins based on naphtha will be studied from the beginning to the end (involving pyrolysis, compression, chilling and fractionation processes).

SIMULASI SISTEM PRODUKSI DI INDUSTRI CAT (STUDI KASUS: UNIT PRODUKSI PT. ABC)

2020 ◽  
Vol 7 (1) ◽  
pp. 11-18
Author(s):  
Yohanes Setiawan ◽  
Waluyo Prasetyo
Keyword(s):  
Customer Satisfaction ◽  
Production Process ◽  
Waiting Time ◽  
Production Unit ◽  
Daily Production ◽  
The Third ◽  
Very High

Productivity in the production process is very influential and become a benchmark for success in every industry. Healthy productivity can increase customer satisfaction because the waiting time for production is not too long from the expected target. The need for improvement in the system is one way to increase production at PT ABC. From the observations results showed the activity percentage of production administration operator were very high. It was at 98.727%. Production per day was less than the company target, the expected target was 40 batches per day, but in fact, it only produced 35 batches average per day. There were three proposed scenarios to fix the performance at Production Unit PT ABC. The Third Scenario was the choice that had highest result for daily production, while Scenario Second was the choice with the highest results if using weekly and annual running times references.

Risk reduction in a cancer chemotherapy production unit: Contribution of an automated drug dispensing system

2020 ◽  
pp. 107815522094106
Author(s):  
Jean-Marie Ouedraogo ◽  
Soumaya El Baraka ◽  
Casimir Adade Adade ◽  
Joseph Sitsopeh ◽  
Abou Sessouma ◽  
...  
Keyword(s):  
Production Process ◽  
Cancer Chemotherapy ◽  
Failure Modes ◽  
Phase 1 ◽  
Production Unit ◽  
Phase 2 ◽  
Risk Minimization ◽  
Drug Dispensing ◽  
Two Phases ◽  
The Impact

Objective The purpose of our study was to evaluate the contribution of an automated drug dispensing system in securing cancer chemotherapy production process at the pharmacy of the National Institute of Oncology in Rabat. Methods The failure modes and effects analysis method was applied to the chemotherapy production process in two phases: Phase 1, using an open shelf for storage then phase 2, using an automated drug dispensing system. The failure modes were defined and their criticality indexes was calculated on the basis of the likelihood of occurrence, the potential severity for the patients and/or the impact on the process and the detection probability. The criticality indexes of the two phases were prioritized and compared. Results We identified 35 failure modes for phase 1 and 37 for phase 2. The sum of criticality indexes was 5957 and 4586, respectively, for phase 1 and phase 2, corresponding to a criticality reduction of −23%. The greatest improvements concerned that the needed drug is missing during the picking, storage of potential expired drugs, and double compounding. Conclusion Our study highlighted the contribution of automated drug dispensing system in risk minimization. The use of automated drug dispensing system is a part of security improvement in chemotherapy production unit.

scholarly journals Waste reduction algorithm used as the case study of simulated bitumen production process

2011 ◽  
Vol 65 (2) ◽  
pp. 197-204
Author(s):  
Marina Savic ◽  
Mica Jovanovic ◽  
Jelena Tanasijevic ◽  
Ozren Ocic ◽  
Aleksandar Spasic ◽  
...  
Keyword(s):  
Environmental Impact ◽  
Production Process ◽  
Vacuum Distillation ◽  
Waste Reduction ◽  
Oil Refinery ◽  
Production Unit ◽  
Distillation Unit ◽  
Reduction Algorithm ◽  
Atmospheric Distillation ◽  
Helping Process

Waste reduction algorithm - WAR is a tool helping process engineers for environmental impact assessment. WAR algorithm is a methodology for determining the potential environmental impact (PEI) of a chemical process. In particular, the bitumen production process was analyzed following three stages: a) atmospheric distillation unit, b) vacuum distillation unit, and c) bitumen production unit. Study was developed for the middle sized oil refinery with capacity of 5000000 tones of crude oil per year. Results highlight the most vulnerable aspects of the environmental pollution that arise during the manufacturing process of bitumen. The overall rates of PEI leaving the system (PEI/h) - Iout PEI/h are: a) 2.14105, b) 7.17104 and c) 2.36103, respectively. The overall rates of PEI generated within the system - Igen PEI/h are: a) 7.75104, b) -4.31104 and c) -4.32102, respectively. Atmospheric distillation unit have the highest overall rate of PEI while the bitumen production unit have the lowest overall rate of PEI. Comparison of Iout PEI/h and Igen PEI/h values for the atmospheric distillation unit, shows that the overall rate of PEI generated in the system is 36.21% of the overall rate of PEI leaving the system. In the cases of vacuum distillation and bitumen production units, the overall rate of PEI generated in system have negative values, i.e. the overall rate of PEI leaving the system is reduced at 60.11% (in the vacuum distillation unit) and at 18.30% (in the bitumen production unit). Analysis of the obtained results for the overall rate of PEI, expressed by weight of the product, confirms conclusions.

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

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
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