New lithium battery recycling equipment technology

The core technology of lithium battery recycling equipment covers three aspects: physical, chemical and intelligent control:

Physical processing technology:

Crushing and screening: The waste lithium battery is broken into small particles by the crusher, and then separated by the size of the vibrating screen. This can initially separate the battery housing and electrode materials, provide suitable particle size materials for subsequent processes, and improve processing efficiency and resource recovery.

Magnetic separation and electrostatic separation: magnetic separation depends on magnetic field force to distinguish magnetic and non-magnetic substances, can separate iron, nickel and other magnetic metals; Electrostatic separation is realized according to the difference of charging of different substances in the electric field, which can effectively separate non-conductors such as plastics and diaphragms from conductors such as metals and electrode materials.

Chemical treatment technology:

Battery material repair and regeneration: the physical and chemical treatment of the recycled electrode material is implemented to repair its crystal structure and electrochemical performance, so that the material can return to the battery production process, reduce the dependence on primary minerals, and reduce costs and environmental pressure.

Metal refining: The use of electrolytic refining, chemical precipitation and other methods to purify and recover metals, produce high-purity metal products that meet industrial standards, used in battery manufacturing, electronics, chemical and other fields.

Intelligent control technology:

Automatic monitoring and testing: the use of sensors and instruments to monitor the temperature, pressure and other parameters of equipment operation in real time, through the remote operation control of automatic control system, to ensure that the equipment is in the best working condition, improve production efficiency and quality, reduce labor risks, and achieve automated and intelligent production.

Data analysis and optimization: Analyze a large amount of production data, establish models, optimize process parameters and equipment operation status, achieve fine management, improve resource recovery rate, reduce energy consumption cost, and provide data support for enterprise decision-making.