System and method for optimizing multiple production indexes in sorting process of raw ore based on man-machine interaction

Abstract

The invention discloses a system and method for optimizing multiple production indexes in the sorting process of raw ore based on man-machine interaction, and belongs to the technical field of ore-dressing production processes. The system comprises a data acquisition module, a parameter setting and modifying unit, an optimization algorithm module library, an optimization model module, an optimization result output unit and a multi-production index optimization system management unit. The method includes the steps that the optimization algorithm module library is searched for a needed optimization algorithm first according to an optimization model; if the algorithm is not found out, the needed optimization algorithm is packaged into a new optimization algorithm module, and if the algorithm is found out, a needed parameter is acquired; an optimization scheme is established; the optimization scheme is operated; a set of production index optimization values are acquired, and meanwhile corresponding raw ore sorting configuration quantity optimal values are issued to an under layer ore dressing production system; if a production index actual value is placed within the production index target value range, the optimization scheme is operated continuously, and if not, the parameter acquiring step is executed again. By the adoption of the system and method, the purposes for saving energy, reducing consumption, lowering production cost and improving economic benefits can be achieved.

Description

technical field [0001] The invention belongs to the technical field of beneficiation production process, and in particular relates to a system and method for optimizing multiple production indicators in the raw ore sorting process based on human-computer interaction. Background technique [0002] With the continuous development of the global economy, the demand for mineral resources in various countries continues to increase, but because mineral resources are non-renewable resources, over time, due to the continuous increase in mining activities, mineral resource reserves are declining in the world . In this form, how to effectively select the usage of various raw ores to realize the optimization process of mineral processing has become an urgent problem to be solved by the mineral processing plant. [0003] The main task of the optimization decision-making of mineral processing production index is to optimize the production resources (namely the allocation of raw ore resou...

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Problems solved by technology

However, some of these five indicators are contradictory. For example, it is impossible to increase the concentrate grade and metal recovery rate at the same time. Excessive pursuit of metal recovery rate will lead to a decline in concentrate grade; low raw ore selection ratio means high price, The pursuit of high returns may lead to high impurities and low grades, etc. However, most of the mineral processing production indicators of existing mineral processing plants are mainly based on manual decision-making, and the preference information is enlarged, resulting in large errors. Therefore, it is impossible for the production planning department to make all the indicators Therefore, in order to solve these problems, designing and developing a multi-production index optimization system in the mineral processing process is of great significance for mineral processing enterprises to quickly and optimally formulate production plans, improve production efficiency, and improve the quality of production index decision-making

[0004] However, according to the current research status of multiple production index optimization systems in the raw ore sorting process at home and abroad, the thinking of the production management decision-making department of my country's mineral processing enterprises in formulating production plans is relatively backward, and in many cases it is manually formulated. The equipment is relatively old and lacks modern information management equipment. Therefore, many production index optimization decision-making methods cannot be well applied to the field, mainly in (1) It is difficult to optimize the production plan: the entire production plan formulation process is Manually completed by production planners, the workload is heavy, and the planning process is

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