861 results about "Paper production" patented technology

The invention relates to an impregnated paper production process multi-parameter collaborative optimization control method based on reinforcement learning, and the method comprises the following steps: S1, building an omnibearing data collection network, achieving the panoramic perception of a production process, and obtaining a real-time data flow; s2, based on a data fusion technology, performing intelligent integration on different sources and different types of data to obtain a fused data set; s3, according to the fused data set and based on a process mechanism, constructing a knowledge graph of the impregnated paper production process; s4, constructing a digital twinborn model of the impregnated paper production line in combination with the fused data set and the knowledge graph; and S5, designing a multi-objective optimization decision framework according to the digital twinborn model, the production plan and the constraint condition, and realizing collaborative optimization of different time scales. According to the invention, multi-parameter collaborative optimization control of the impregnated paper production process is realized.

The invention relates to the technical field of self-adhesive label paper detection devices, in particular to a visual detection device for self-adhesive label paper production, which comprises a workbench, a winding part and an unwinding part, and is characterized in that the workbench is provided with a first visual detection module and a second visual detection module; a plurality of guide rollers used for adjusting the direction of label paper are arranged on the workbench, a positioning locking piece is arranged in the middle of the workbench, a continuous detection unit is arranged on the portion, close to the first visual detection module, of the workbench, and a vacancy patching unit is arranged on the portion, close to the second visual detection module, of the workbench. According to the visual detection device for self-adhesive label paper production, aiming at the vacancy problem existing on a label paper roll, the device is provided with the positioning locking piece, the continuous detection unit and the vacancy supplementing and pasting unit which are matched for use, the label paper is allowed to be rapidly supplemented and pasted in the detection process, shutdown operation is not needed, the pain point of'shutdown supplementing 'of traditional equipment is solved, and the production efficiency is improved. And the detection efficiency is obviously improved.

The invention relates to the technical field of papermaking wastewater treatment, in particular to a waste pulp residue treatment device for raw paper production, which comprises waste pulp treatment equipment for raw paper production, the waste pulp treatment equipment comprises a treatment tank, and the top side of the treatment tank is provided with automatic adding equipment matched with a stirring assembly for use. And driving equipment matched with the automatic adding equipment for use is mounted on the side wall of the treatment tank. According to the waste pulp residue treatment device for raw paper production, the stroke of driving equipment is prolonged, the displacement of the sliding frame is increased, so that the sliding base is pushed to act, the lifting table is actively triggered to ascend and descend through the connecting rod, the dosing action and the stirring strength are dynamically bound, automatic dosing is achieved when the stirring requirement is high, and invalid dosing is avoided; in addition, the lifting platform drives the stirring rod to rotate and axially reciprocate, waste slurry fiber agglomerates are effectively scattered, precipitation prevention is achieved, meanwhile, longitudinal eddy current can be formed through axial reciprocating of the stirring rod, agent diffusion is enhanced, the mixing efficiency is improved, the reaction period is shortened, and energy loss is reduced.

The invention discloses an artificial intelligence-driven thermal paper production waste intelligent identification method, and relates to the technical field of thermal paper production waste identification, and the method comprises the following steps: obtaining a first normal reference value and a second normal reference value based on a first amount of normal thermal paper drawing; obtaining an initial abnormal region based on the real-time thermo-sensitive paper graph, the first normal reference value and the second normal reference value; acquiring three-dimensional point cloud charts above and below the thermo-sensitive paper in the initial abnormal region part, and marking the three-dimensional point cloud charts as real-time point cloud charts; constructing a real-time detection angle based on the real-time point cloud atlas; acquiring a historical normal angle based on the second quantity of normal thermal sensitive paper; acquiring a normal angle reference value based on the historical normal angle; based on the real-time detection angle value and a normal angle reference value, judging whether the thermo-sensitive paper is a waste material or not; the method is used for solving the problem that in an existing thermo-sensitive paper production waste recognition technology, whether interference exists or not cannot be further analyzed to cause recognition error defects, and consequently the accuracy of thermo-sensitive paper production waste recognition is low.

A family of paper tape turn-up constructs for accomplishing automated paper web turnup in paper processing. Methods and apparatus to form the constructs are described. Methods and Apparatus to apply the constructs to paper production equipment are also described. In some examples, the paper tape turn-up construct includes a cover flap to protect an adhesive layer during initiation of the paper web turn up process.

The utility model relates to a cutting device for test paper production, which comprises a device body, the upper surface of the device body is fixedly connected with a conveying frame, the inner wall of the device body is clamped with a conveying belt, and the inner wall of the device body is fixedly connected with a mounting frame. According to the device, after parameters are set through the touch panel, automatic and orderly operation can be carried out, the human input is greatly reduced, the production efficiency is improved, and the device is particularly suitable for the large-scale test paper production requirement, meanwhile, an electric hydraulic telescopic rod is matched with an auxiliary pulley to precisely fix test paper, and an electric sliding frame drives a blade to stably move in an electric sliding groove; the test paper cutting device has the advantages that test paper displacement during cutting is effectively avoided, it is guaranteed that the edges of the test paper are neat, the size precision is high, the product quality is improved, the cutting protection plate and the cooling fan protect and cool the blade in the cutting process, the service life of the blade is prolonged, finally, the cut test paper is smoothly discharged under the assistance of the discharging auxiliary fan, and the problems of paper jamming and the like are solved.

The invention relates to an impregnated paper production quality detection method and system based on deep learning, and the method comprises the following steps: S1, obtaining an industrial camera image flow, sensor and process parameters, and event and work order information, and constructing a synchronous data packet; s2, preprocessing the synchronous data packet to obtain preprocessed image data, process data and an ROI mask; s3, obtaining a detection frame set and a segmentation mask set based on multi-model AI detection and segmentation according to the preprocessed image data, the process data and the ROI mask; s4, segmenting the mask set according to the detection frame set, and performing track clustering along the paper feeding direction to form a defect event flow; s5, on the basis of the defect event stream, performing online quality prediction and root cause analysis, and obtaining future interval quality prediction and key factor sorting; and S6, according to future interval quality prediction and key factor sorting, adaptive process optimization is carried out, and an optimal control instruction is obtained. According to the invention, the detection accuracy and fineness are effectively improved, and intelligent identification and cross-frame tracking of defects are realized.

The invention provides high-humidity base paper and a quantifiable preparation process thereof, and relates to the field of paper production and processing. Comprising the following raw materials in percentage by mass: 15%-25% of softwood pulp, 40%-55% of hardwood pulp, 10%-25% of soft fibers, 10%-20% of nanocellulose, 0.5%-1.5% of vegetable fat polymer composite microspheres and 1%-3% of functional aids, wherein the solid content of aqueous suspension of the softwood pulp and the hardwood pulp is 3%-8%; the nano cellulose is subjected to surface modification treatment. According to the paper, coniferous wood pulp long fibers, hardwood pulp short fibers and soft fibers are utilized to form a three-dimensional framework, nano-crystalline cellulose enhances wet load transmission through a nanoscale bridging network, vegetable fat microspheres construct a hydrophobic layer to inhibit moisture permeation, the three components have a synergistic effect, so that the wet tensile strength is improved, and meanwhile, high air permeability is kept; and the balance of water resistance and ventilation is realized.

The invention discloses a quality inspection and early warning system for impregnated paper production, and relates to the field of quality inspection and early warning for impregnated paper production, and the method comprises the steps: directly measuring the real liquid pressure intensity of a paper immersion area through an impregnation parameter collection module, and generating a dynamic pressure intensity deviation in combination with the theoretical pressure intensity calculated by a dynamic pressure intensity calibration module; the valve opening reference value is matched based on the dynamic pressure deviation, the solvent flow is corrected in real time in combination with the liquid level change rate, and the precipitation risk is evaluated by analyzing the concentration reduction amplitude after solvent supplementation and the duration required by solution stabilization; when the risk reaches an activation condition, triggering an anti-precipitation instruction, according to the anti-precipitation instruction, linking a stirrer to stir in a production intermittent period, and injecting a solute to ensure that the concentration reaches the standard; and recording the water level adjusting time length, the concentration compensation time length and the instruction response time length, triggering graded alarm when time is out, and outputting an interrupt reset instruction to a corresponding adjusting module. And the quality control level in the impregnated paper production process is improved.

The invention provides an intelligent production scheduling data analysis method for a platen paper production line, and the method comprises the steps: obtaining the fiber length distribution and the current screen mesh aperture size from a slurry pool, obtaining the real-time capacity level and the production speed of each workshop section from a storage bin monitoring device, and carrying out the data fusion, thereby obtaining an initial dynamic relation matrix; if the agglomeration density is higher than a preset risk threshold value, relevant subsets about the fiber flowing speed and the screen resistance are extracted from the dynamic relation matrix, the size of the screen hole diameter is adjusted through the fiber agglomeration density, meanwhile, the production speed is integrated, and the adjusted material passing rate is obtained; the material passing rate is compared with the current storage bin capacity level to obtain the capacity matching degree, whether the buffer storage space needs to be expanded or not is judged according to the capacity matching degree, and if the material passing rate exceeds the capacity level preset threshold value, the buffer space distribution proportion is increased.