scholarly journals Primal and Dual Economic Measures of Capacity Utilization in Tunisia

2013 ◽  
Vol 5 (7) ◽  
Author(s):  
Kamel Helali ◽  
Mohamed Siala ◽  
Maha Kalai
1988 ◽  
Vol 20 (10) ◽  
pp. 175-182
Author(s):  
V. K. Karia ◽  
V. S. Joshi

The Gujarat Narmada Valley Fertilizers Company Ltd (GNFC), a giant fertilizers and chemicals complex, owns the world's largest single stream ammonia and urea plants, and is located in the predominantly backward area of Bharuch District in Gujarat State, India. The company began commercial production on 1st July 1982. GNFC's performance as regards both capacity utilization and pollution control, has been good right from commissioning. Concerning pollution control, the company has adopted a productivity oriented approach which is entirely different from the usual ‘policeman' approach or ‘problem' approach. The company decided to find applications for each of the pollutants. Regarding liquid effluents, the company has successfully concluded a number of research and development projects as a result of which nearly 71% of the total volume of liquid effluent is put to productive use. This has helped the company to reduce the fresh water intake by about 18,000 m3/day (much more than the total volume of fresh water required by the whole of Bharuch city!). The remaining volume (29%) leaving GNFC premises completely conforms to irrigation standards and has been utilized by farmers for the last 5 years. This has resulted in a ‘mini green revolution' in the water-scarce area of Bharuch District. Since the entire effluent is either being recycled within the company or being used by farmers, the company has reached a stage of ‘zero effluent'. The company has also saved more than US$ 500,000 to date, by properly managing liquid effluent.


Author(s):  
Rafael Alberdi ◽  
Elvira Fernandez ◽  
Igor Albizu ◽  
Miren Terese Bedialauneta ◽  
Roberto Fernandez

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Darina Dvinskikh ◽  
Alexander Gasnikov

Abstract We introduce primal and dual stochastic gradient oracle methods for decentralized convex optimization problems. Both for primal and dual oracles, the proposed methods are optimal in terms of the number of communication steps. However, for all classes of the objective, the optimality in terms of the number of oracle calls per node takes place only up to a logarithmic factor and the notion of smoothness. By using mini-batching technique, we show that the proposed methods with stochastic oracle can be additionally parallelized at each node. The considered algorithms can be applied to many data science problems and inverse problems.


1998 ◽  
Vol 13 (2) ◽  
pp. 115-134 ◽  
Author(s):  
ADESOJI ADELAJA ◽  
BONNIE MCCAY ◽  
JULIA MENZO

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