| 1. |
- Fatahi, R., et al.
(författare)
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Ventilation Prediction for an Industrial Cement Raw Ball Mill by BNN—A “Conscious Lab” Approach
- 2021
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Ingår i: Materials. - : MDPI. - 1996-1944. ; 14:12
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Tidskriftsartikel (refereegranskat)abstract
- In cement mills, ventilation is a critical key for maintaining temperature and material transportation. However, relationships between operational variables and ventilation factors for an industrial cement ball mill were not addressed until today. This investigation is going to fill this gap based on a newly developed concept named “conscious laboratory (CL)”. For constructing the CL, a boosted neural network (BNN), as a recently developed comprehensive artificial intelligence model, was applied through over 35 different variables, with more than 2000 records monitored for an industrial cement ball mill. BNN could assess multivariable nonlinear relationships among this vast dataset, and indicated mill outlet pressure and the ampere of the separator fan had the highest rank for the ventilation prediction. BNN could accurately model ventilation factors based on the operational variables with a root mean square error (RMSE) of 0.6. BNN showed a lower error than other traditional machine learning models (RMSE: random forest 0.71, support vector regression: 0.76). Since improving the milling efficiency has an essential role in machine development and energy utilization, these results can open a new window to the optimal designing of comminution units for the material technologies.
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| 2. |
- Razavi, R., et al.
(författare)
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Energy-efficient scheduling of real-time cloud services using task consolidation and Dynamic Voltage Scaling
- 2014
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Ingår i: 2014 7th International Symposium on Telecommunications, IST 2014. - 9781479953592 ; , s. 675-682
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Konferensbidrag (refereegranskat)abstract
- Energy consumption has attracted a lot of attention in the past few years, because energy reduction causes a significant mitigation of the negative impact on the environment along with an operational cost reduction. Energy-efficient task scheduling is an effective technique to decrease the energy consumption in the Cloud Computing Systems (CCSs). In this paper, the problem of scheduling a set of precedence-constrained real-time services onto a set of heterogenous servers is investigated. Each service contains a set of tasks bounded with a specific deadline. The main notion applied in this paper is to employ the consolidation approach along with the Dynamic Voltage Scaling (DVS) technique. The proposed scheduler is developed in three phases. Tasks' deadlines and a laxity metric are computed for each service according to the corresponding service deadline prior to the main scheduling phase. Afterwards, in order to consolidate the tasks onto the minimum number of servers, the algorithm estimates the required number of servers. Finally, in the last phase, the tasks are scheduled while the DVS technique is applied with considering the tasks' deadlines. The extensive experimental results clearly demonstrate that the proposed algorithm reduces the energy consumption of a CCS by 14% on average in comparison with beam search algorithm. In addition, it outperforms the non power-Aware algorithm by 84%.
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