7results about How to "Solving Mixing Problems" patented technology

The utility model relates to plant protein technology field especially relates to a kind of concentrated device for extracting composite plant protein, it includes: stirring drum, the inside installation of stirring drum is cleaned ring, multiple cleaning brushes are installed in the circumferential surface of cleaned ring, the circumferential surface of cleaned ring between cleaning brush is equipped with water outlet hole, water outlet hole is connected with the water inlet hole of cleaned ring top by passage, water inlet hole connects the one end of water inlet pipe, the top of cleaned ring is connected with the telescopic end of moving cylinder, the fixed end of moving cylinder is connected with L type support frame horizontal portion, the top of stirring drum is equipped with liquid inlet, the side surface of stirring drum is cleaned by cleaning brush on cleaned ring, and by being provided with water outlet hole, it can be sprayed to outside by water outlet hole in cleaning process, the inner wall of stirring drum in cleaning process can be washed, improve cleaning effect, effectively solve the problem that the tank wall of existing cleaning method is not easy to clean stirring device, influence subsequent plant protein and water mixing.

This invention relates to the field of sampling equipment technology, specifically to an intelligent sampling device for wheat distillers' grains fermentation. The intelligent sampling device includes a multi-stage sampling rod and an outer shell. The multi-stage sampling rod is composed of multiple sampling rods, which are nested and connected sequentially and slide relative to each other. The outermost sampling rod is nested inside the outer shell. Each sampling rod has a protruding conical portion in the middle of its segment, which cooperates with the buoyancy of the fermentation liquid. Both the sampling rod and the inner wall of the outer shell have grooved portions. Multiple liquid sampling ports are evenly distributed circumferentially at the conical angle of the conical portion of each sampling rod. A door is installed at each liquid sampling port, and a guide block is provided on the door. The door slides in cooperation with the grooved portion of the previous sampling rod or the outer shell through the guide block. This invention avoids sample mixing, improves sampling efficiency and accuracy, facilitates operation, and ensures sample purity.

The utility model relates to the technical field of soil sampling, in particular to a soil sampling device for forestry ecological construction, which comprises a sampling tube, lower sealing sheets are uniformly and rotatably mounted at the bottom end of the inner side wall of the sampling tube, upper sealing sheets are rotatably mounted on the lower sealing sheets, a compression ring is slidably mounted on the inner side wall of the sampling tube, and a lower sealing sheet is rotatably mounted on the compression ring. The pressure ring is rotatably connected with the upper sealing sheet, a plugging sleeve is arranged on the inner side wall of the sampling tube, a pressure rod is slidably mounted on the sampling tube, a transmission rod is mounted between the pressure rod and the pressure ring, and a sampling cover is mounted at the side end of the sampling tube; after the sampling tube is inserted into soil, the opening at the bottom end of the sampling tube can be forcibly closed, a loose desertification soil sample is reliably blocked in the tube, and meanwhile, due to the side opening type design, after the sampling tube is taken out, soil at different depths can be directly observed and accurately scraped in a layered manner through the long opening in the side wall without disturbing the sample; therefore, the desertification soil sampler is suitable for accurate sampling of desertification soil.

The present application provides an oxygen system scheduling optimization method based on deep reinforcement learning hybrid action space, comprising the following steps: oxygen system scheduling optimization; drl-based method; results and analysis. The present application is based on deep reinforcement learning, and proposes an HAC algorithm for solving the oxygen system scheduling problem. The proposed algorithm extends the actor network of the AC algorithm, and designs an additional segmented reward function according to the market time-of-use electricity price. The HAC algorithm solves the mixed problem of discrete variables and continuous variables, and realizes energy saving and emission reduction while improving economic benefits. Experimental results show that the HAC algorithm places the scheduling problem in a hybrid action space, avoiding the problem of suboptimal action selection. Compared with the traditional algorithm, the HAC algorithm significantly improves the convergence and accuracy of the algorithm, making the oxygen system have higher oxygen utilization rate and economic benefits.

The present application relates to a kind of material control systems for crystallization kettle, including crystallization kettle, feeding tank and the feeding mechanism being set above crystallization kettle, feeding mechanism includes distribution component a, distribution component b and drive assembly, distribution component a and distribution component b slide in feeding tank, drive assembly drives distribution component a and distribution component b to do reciprocating transverse motion and control the amount of feeding by switching with the connection between feeding pipeline;Cleaning component a and cleaning component b all include water reservoir, distribution component a and distribution component b are cleaned by flushing under the action of water reservoir in its inside;Sliding assembly is arranged between distribution component a and distribution component b, for when distribution component a and distribution component b are not connected with feeding pipeline, material is blocked.The present application overcomes the problem that material can only be quantitatively transported, cannot control the amount of required crystallization, raw material mixing is not uniform and cannot clean the device.