Optimal Scheduling of Multiproduct Pipeline System Using MILP Continuous Approach

Part 5: Planning and Scheduling; International audience; To date, the multiproduct pipeline transportation mode has nationally and internationally considerably evolved thanks to his efficiently and effectively of transporting several products. In this paper, we focus our study on the scheduling of a multiproduct pipeline system that receives a number of petroleum products (fuels) from a single refinery source in order to be distributed to several storage and distribution centers (depots). Mixed Integer Linear Programming (MILP) continuous mathematical approach is presented to solve this problem. The sequence of injected products in the same pipeline should be carefully studied, in order to meet market demands and ensure storage autonomy of the marketable pure products in the fuels depots on the one hand and to minimize the number of interfaces; Birth zone of mixture between two products in contact and in sequential flow, which may hinder the continuous operation of the pipeline system, by the necessity of additional storage capacity for this last mixture, that is in no way marketable and requires special processing operations. This work is applied on a real case of a multiproduct pipeline that feeds the western and southwestern region of Algeria with fuels. The obtained results based on the MILP continuous approach give an optimal scheduling of the multiproduct transport system with a minimized number of interfaces. Document type: Conference objec

Areas, aspects, and the stages of the crisis of Salar in Khorasan emphasizing the concepts and management of crisis (1845 – 1849)

The crisis of Salar in Khorasan is one of the most significant ones during Qajar dynasty. It can be analyzed through different angles, considering its vastness and diverse aspects. In the present article, it has been tried to investigate different stages of the formation of the crisis on order to detect the most important one. The main factor in this article is taking advantage of a theoretical framework in the discussions on crisis and management of crisis and also attempting to analyze historic events based on an interdisciplinary framework composed of history, sociology and political sciences. The author insists on showing theoretical topics more prominent and defining different aspects of the concepts and management of crisis to enrich the topics discussed and enjoying other sciences to empower historic analysis performed. The main for expansion of the crisis of Salar is poor performance and deficient crisis management of the central government in the period of Mohammad shah and chancellorship of Aghasi. Another considerable point about the crisis of Salar is that it was formed based on disagreements and discords inside the government and among tribes. Finally, the crisis was controlled by the management of Mirza Taghi Khan Amir Kabir, which is referred to as a successful management of crisis

Learning discriminatory deep clustering models

Deep convolutional auto-encoder (DCAE) allows to obtain useful features via its internal layer and provide an abstracted latent representation, which has been exploited for clustering analysis. DCAE allows a deep clustering method to extract similar patterns in lowerdimensional representation and find idealistic representative centers for distributed data. In this paper, we present a deep clustering model carried out in presence of varying degrees of supervision. We propose a new version of DCAE to include a supervision component. It introduces a mechanism to inject various levels of supervision into the learning process. This mechanism helps to effectively reconcile extracted latent representations and provided supervising knowledge in order to produce the best discriminative attributes. The key idea of our approach is distinguishing the discriminatory power of numerous structures, through varying degrees of supervision, when searching for a compact structure to form robust clusters. We evaluate our model on MNIST, USPS, MNIST fashion, SVHN datasets and show clustering accuracy on different supervisory levels