A nonlinear goal-programming-based DE and ANN approach to grade optimization in iron mining

He, Yong; Gao, Siwei; Liao, Nuo; Liu, Hongwei

doi:10.1007/s00521-015-2006-9

Cited by 10 publications

(12 citation statements)

References 46 publications

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“…Civil engineering [66,67,151], Industrial applications [148], Iron Mining [70], Manufacturing [73] Nuclear domain [143] works are also relevant in terms of using a huge up-to-date variety of techniques to exploit data that are continuously being generated by tools, applications, sensors, and people.…”

Section: General Application To Engineeringmentioning

confidence: 99%

Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments

Álvarez-Rodríguez

Alor-Hernández

Mejia-Miranda

2018

Scientific Programming

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The present paper introduces and reviews existing technology and research works in the field of scientific programming methods and techniques in data-intensive engineering environments. More specifically, this survey aims to collect those relevant approaches that have faced the challenge of delivering more advanced and intelligent methods taking advantage of the existing large datasets. Although existing tools and techniques have demonstrated their ability to manage complex engineering processes for the development and operation of safety-critical systems, there is an emerging need to know how existing computational science methods will behave to manage large amounts of data. That is why, authors review both existing open issues in the context of engineering with special focus on scientific programming techniques and hybrid approaches. 1193 journal papers have been found as the representative in these areas screening 935 to finally make a full review of 122. Afterwards, a comprehensive mapping between techniques and engineering and nonengineering domains has been conducted to classify and perform a meta-analysis of the current state of the art. As the main result of this work, a set of 10 challenges for future data-intensive engineering environments have been outlined.

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Section: General Application To Engineeringmentioning

confidence: 99%

Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments

Álvarez-Rodríguez

Alor-Hernández

Mejia-Miranda

2018

Scientific Programming

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“…Yousefi et al [42] suggested a hybrid GP-DEA model in a network to present an improvement in solutions and to rank units (all efficient and inefficient ones) based on the requirements of experts. In the past few years, numerous researchers have used the GP method and rendered new models and for such paradigms, one can refer to Maiti and Roy [43,44]; Chen et al [45]; Trivedi and Singh [46]; He et al [47] and Roy et al [48,49]. Methods in relevance to GP modes are extremely diverse and even make provisions to optimize contradictory goals.…”

Section: Introductionmentioning

confidence: 99%

Efficiency Evaluation of a Three-Stage Leader-Follower Model by the Data Envelopment Analysis with Double-Frontier Viewpoint

et al. 2019

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In this paper, a three-stage network with optimal desirable and undesirable inputs and outputs has been taken into consideration by us. This network comprises of a leader and two followers. Four diverse models of Data Envelopment Analysis (DEA) to measure the efficiency or the performance, of this three-stage network have been taken under contemplation; these are namely, a Black Box Model and three Stackelberg Game (Theory) Models. A multiplicative DEA, with a double-frontier approach, to measure the efficiency of the entire system and the performances of the decision making units (DMUs), from both the optimistic and pessimistic views have been utilized. In this paper attempts have been made to present the goals of the managers in the models. Hence, aspects of goal programming have been manipulated so as to define cooperation between the leader and followers, such that, we are able to include the objectives of the managers in the models. In actual fact, a noncooperative collaboration is deliberated upon. In addition to which, in the second and third scenarios, the leader-follower, nonlinear models are present. Thereby, a heuristic approach is suggested to convert the nonlinear models into linear ones.

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“…The results show that the Pareto-optimal solutions at higher profits (with lower resource utilization rates) are more sensitive to the unit copper concentrate prices than those obtained in regions with lower profits.Processes 2018, 6, 228 2 of 22 realistic problem to optimize the production process of metal mines for mining mineral resources with the greatest economic benefits and resource efficiency to better utilize the mineral resources for sustainable development [2].The production process of metal mines is a complex industrial process, consisting of three unit processes in series, i.e., the exploration process, the mining process and the beneficiation process. The input of the latter unit process is the output of the previous one [3,4]. The optimization of the production process of metal mines is to determine the best production technology indicators that have a significant impact on economic benefits and resource efficiency [5,6].…”

mentioning

confidence: 99%

“…These works emphasized the optimization targeting at maximizing economic benefits. The third aspect is the optimization of the production process of metal mines considering economic benefit and resource efficiency with either constraint or weight methods [4,[20][21][22][23]. These methods convert multiple objectives into a single objective, thus the optimization results depend largely on subjective assignment of the constraint or weight value [8].The above-mentioned works have progressed the optimization method of the production process of metal mines and some have attempted to use these methods for application.…”

mentioning

confidence: 99%

“…The production process of metal mines is a complex industrial process, consisting of three unit processes in series, i.e., the exploration process, the mining process and the beneficiation process. The input of the latter unit process is the output of the previous one [3,4]. The optimization of the production process of metal mines is to determine the best production technology indicators that have a significant impact on economic benefits and resource efficiency [5,6].…”

mentioning

confidence: 99%

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Production Process Optimization of Metal Mines Considering Economic Benefit and Resource Efficiency Using an NSGA-II Model

Xun-hong

Liu

et al. 2018

Processes

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The optimization of the production process of metal mines has been traditionally driven only by economic benefits while ignoring resource efficiency. However, it has become increasingly aware of the importance of resource efficiency since mineral resource reserves continue to decrease while the demand continues to grow. To better utilize the mineral resources for sustainable development, this paper proposes a multi-objective optimization model of the production process of metal mines considering both economic benefits and resource efficiency. Specifically, the goals of the proposed model are to maximize the profit and resource utilization rate. Then, the fast and elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II) is used to optimize the multi-objective optimization model. The proposed model has been applied to the optimization of the production process of a stage in the Huogeqi Copper Mine. The optimization results provide a set of Pareto-optimal solutions that can meet varying needs of decision makers. Moreover, compared with those of the current production indicators, the profit and resource utilization rate of some points in the optimization results can increase respectively by 2.99% and 2.64%. Additionally, the effects of the decision variables (geological cut-off grade, minimum industrial grade and loss ratio) on objective functions (profit and resource utilization rate) were discussed using variance analysis. The sensitivities of the Pareto-optimal solutions to the unit copper concentrate price were studied. The results show that the Pareto-optimal solutions at higher profits (with lower resource utilization rates) are more sensitive to the unit copper concentrate prices than those obtained in regions with lower profits.Processes 2018, 6, 228 2 of 22 realistic problem to optimize the production process of metal mines for mining mineral resources with the greatest economic benefits and resource efficiency to better utilize the mineral resources for sustainable development [2].The production process of metal mines is a complex industrial process, consisting of three unit processes in series, i.e., the exploration process, the mining process and the beneficiation process. The input of the latter unit process is the output of the previous one [3,4]. The optimization of the production process of metal mines is to determine the best production technology indicators that have a significant impact on economic benefits and resource efficiency [5,6]. Technical production indicators include the recoverable reserves, average ore grade, geological cut-off grade, minimum industrial grade, loss ratio, dilution ratio, raw ore grade and volume, concentrate grade and volume, and concentration ratio. As the market changes and production technology advances, it is necessary to adjust and optimize these indicators in time to achieve the best results. The optimization of the metal mines production process is an effective way to raise the economic benefits of enterprises and contribute to the sustainable development of res...

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A nonlinear goal-programming-based DE and ANN approach to grade optimization in iron mining

Cited by 10 publications

References 46 publications

Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments

Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments

Efficiency Evaluation of a Three-Stage Leader-Follower Model by the Data Envelopment Analysis with Double-Frontier Viewpoint

Production Process Optimization of Metal Mines Considering Economic Benefit and Resource Efficiency Using an NSGA-II Model

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