Energy consumption hierarchical analysis based on interpretative structural model for ethylene production

Interpretative structural model(ISM) can transform a multivariate problem into several sub-variable problems to analyze a complex industrial structure in a more efficient way by building a multi-level hierarchical structure model. To build an ISM of a production system, the partial correlation coefficient method is proposed to obtain the adjacency matrix, which can be transformed to ISM. According to estimation of correlation coefficient, the result can give actual variable correlations and eliminate effects of intermediate variables. Furthermore, this paper proposes an effective approach using ISM to analyze the main factors and basic mechanisms that affect the energy consumption in an ethylene production system. The case study shows that the proposed energy consumption analysis method is valid and efficient in improvement of energy efficiency in ethylene production.

Elasticity Ratio of Resource Consumption and the Resource Consumption

Today the resources are becoming scarcer, which should not be regarded as unexhausted any more. Correspondingly, the production would be constrained by the scarcity of resources clearly. Then the economic researchers would pay much more attention to reducing the consumption of natural resources in the future. Therefore this paper brings foreword the conception of elasticity ratio of resource consumption based on the concept of elasticity and analyzes the relationship between the parameters. For the certain relationships between the elasticity ratio of resource consumption and resource consumption, this paper will try to reveal, to keep economy growing while resource consumption reducing, what conditions should be met as to the relationships among resource productivity, its growth rate, energy saving efficiency, economic growth rate and elasticity ratio of resource consumption. This paper proves the relationship between the China＇s energy consumption and economy growth using statistic data from 1978 to 2003.

Evaluation of rope shovel operators in surface coal mining using a Multi-Attribute Decision-Making model

Rope shovels are used to dig and load materials in surface mines. One of the main factors that influence the production rate and energy consumption of rope shovels is the performance of the operator. This paper presents a method for evaluating rope shovel operators using the Multi-Attribute Decision-Making （MADM） model. Data used in this research were collected from an operating surface coal mine in the southern United States. The MADM model consists of attributes, their weights of importance, and alter- natives. Shovel operators are considered the alternatives, The energy consumption model was developed with multiple regression analysis, and its variables were included in the MADM model as attributes. Preferences with respect to min/max of the defined attributes were obtained with multi-objective opti- mization. Multi-objective optimization was conducted with the overall goal of minimizing energy con- sumption and maximizing production rate. Weights of importance of the attributes were determined by the Analytical Hierarchy Process （AHP）, The overall evaluation of operators was performed by one of the MADM models, i.e., PROMETHEE If. The research results presented here may be used by mining professionals to held evaluate the performance of rode shovel operators in surface mining.