It is important to establish the evaluation system of the cleaner production of the mining enterprise, which canprovide the technical support and guidance for the cleaner production evaluation and facilitating the pro...It is important to establish the evaluation system of the cleaner production of the mining enterprise, which canprovide the technical support and guidance for the cleaner production evaluation and facilitating the promotion ofcleaner productive techniques so as to realize the integration of economic development and environmental protection.This paper, according to the characteristic of mining and Analytic Hierarchy Process (AHP), establishes the evaluationindex system, puts forward the overall quantitative evaluation method based on Fuzzy Mathematics and the experts’experience, and establishes the evaluation system of cleaner production. The main problems in cleaner production of themine are analyzed by calculation, and some countermeasures and suggestions are proposed.展开更多
This paper mainly describes that loose of jig bed affects jig's separation effect, and the corresponding fuzzy rules were built. Using the evaluating index of jig's separation effect--imperfection (I) and tota...This paper mainly describes that loose of jig bed affects jig's separation effect, and the corresponding fuzzy rules were built. Using the evaluating index of jig's separation effect--imperfection (I) and total misplaced material (Cz), it evaluates status of loose of jig bed by fuzzy inference system. Experimental simulation and applications in practice prove the method's feasibility.展开更多
An integrated evaluation system under randomness and fuzziness was developed in this work to systematically assess the risk of groundwater contamination in a little town, Central China. In this system, randomness of t...An integrated evaluation system under randomness and fuzziness was developed in this work to systematically assess the risk of groundwater contamination in a little town, Central China. In this system, randomness of the parameters and the fuzziness of the risk were considered simultaneously, and the exceeding standard probability of contamination and human health risk due to the contamination were integrated. The contamination risk was defined as a combination of "vulnerability" and "hazard". To calculate the value of "vulnerability", pollutant concentration was simulated by MODFLOW with random input variables and a new modified health risk assessment(MRA) model was established to analyze the level of "hazard". The limit concentration based on environmental-guideline and health risk due to manganese were systematically examined to obtain the general risk levels through a fuzzy rule base. The "vulnerability" and "hazard" were divided into five categories of "high", "medium-high", "medium", "low-medium" and "low", respectively. Then, "vulnerability" and "hazard" were firstly combined by integrated evaluation. Compared with the other two scenarios under deterministic methods, the risk obtained in the proposed system is higher. This research illustrated that ignoring of uncertainties in evaluation process might underestimate the risk level.展开更多
This paper introduces a new methodology for the damage assessment of existing-transmission structures using six layers, zero order Sugeno model. The model is a hybrid fuzzy-neural system that combines the power of neu...This paper introduces a new methodology for the damage assessment of existing-transmission structures using six layers, zero order Sugeno model. The model is a hybrid fuzzy-neural system that combines the power of neural networks and fuzzy systems. It is a learning expert system that finds the parameters of the fuzzy sets and fuzzy rules by exploiting approximation techniques from neural networks. The condition ratings of the structural components are determined based on visually observed deterioration-symptoms and the severity of those symptoms. A supervised learning process using training data and expert opinions is used to develop the expert system rules and determine the ratings of the structural components. For the learning from training data, the model uses a combination of least-square estimator and gradient descent method. A sequential least square algorithm is used to determine the weighting factors that minimized the errors. A test case is given to illustrate the power of the proposed fuzzy-neural system. It is concluded that the Sugeno model＇s ability to tune the parameters based on the training data makes it superior to the rules produced by an expert in the conventional fuzzy logic systems.展开更多
文摘It is important to establish the evaluation system of the cleaner production of the mining enterprise, which canprovide the technical support and guidance for the cleaner production evaluation and facilitating the promotion ofcleaner productive techniques so as to realize the integration of economic development and environmental protection.This paper, according to the characteristic of mining and Analytic Hierarchy Process (AHP), establishes the evaluationindex system, puts forward the overall quantitative evaluation method based on Fuzzy Mathematics and the experts’experience, and establishes the evaluation system of cleaner production. The main problems in cleaner production of themine are analyzed by calculation, and some countermeasures and suggestions are proposed.
文摘This paper mainly describes that loose of jig bed affects jig's separation effect, and the corresponding fuzzy rules were built. Using the evaluating index of jig's separation effect--imperfection (I) and total misplaced material (Cz), it evaluates status of loose of jig bed by fuzzy inference system. Experimental simulation and applications in practice prove the method's feasibility.
基金Projects(51039001,51009063) supported by the National Natural Science Foundation of ChinaProject(SX2010-026) supported by State Council Three Gorges Project Construction Committee Executive Office,China+1 种基金Project(2012BS046) supported by Henan University of Technology,ChinaProject(BYHGLC-2010-02) supported by the Guangzhou Water Authority,China
文摘An integrated evaluation system under randomness and fuzziness was developed in this work to systematically assess the risk of groundwater contamination in a little town, Central China. In this system, randomness of the parameters and the fuzziness of the risk were considered simultaneously, and the exceeding standard probability of contamination and human health risk due to the contamination were integrated. The contamination risk was defined as a combination of "vulnerability" and "hazard". To calculate the value of "vulnerability", pollutant concentration was simulated by MODFLOW with random input variables and a new modified health risk assessment(MRA) model was established to analyze the level of "hazard". The limit concentration based on environmental-guideline and health risk due to manganese were systematically examined to obtain the general risk levels through a fuzzy rule base. The "vulnerability" and "hazard" were divided into five categories of "high", "medium-high", "medium", "low-medium" and "low", respectively. Then, "vulnerability" and "hazard" were firstly combined by integrated evaluation. Compared with the other two scenarios under deterministic methods, the risk obtained in the proposed system is higher. This research illustrated that ignoring of uncertainties in evaluation process might underestimate the risk level.
文摘This paper introduces a new methodology for the damage assessment of existing-transmission structures using six layers, zero order Sugeno model. The model is a hybrid fuzzy-neural system that combines the power of neural networks and fuzzy systems. It is a learning expert system that finds the parameters of the fuzzy sets and fuzzy rules by exploiting approximation techniques from neural networks. The condition ratings of the structural components are determined based on visually observed deterioration-symptoms and the severity of those symptoms. A supervised learning process using training data and expert opinions is used to develop the expert system rules and determine the ratings of the structural components. For the learning from training data, the model uses a combination of least-square estimator and gradient descent method. A sequential least square algorithm is used to determine the weighting factors that minimized the errors. A test case is given to illustrate the power of the proposed fuzzy-neural system. It is concluded that the Sugeno model＇s ability to tune the parameters based on the training data makes it superior to the rules produced by an expert in the conventional fuzzy logic systems.
