TWM624279U - Smart Microalgae Cultivation System - Google Patents

Abstract

This creation is an intelligent microalgae cultivation system, which includes a microalgae cultivation farm, a nutrient solution equipment, at least one monitoring equipment and a control computer system, mainly through the convolutional neural network installed in the control computer system. The network (CNN) identifies the image of at least one culture container recorded by the monitoring equipment, and identifies the concentration of microalgae in the at least one culture container, and then the control computer system determines the concentration of microalgae in the at least one culture container according to the identified concentration of microalgae in the at least one culture container. The nutrient solution equipment can be operated by changing the change of the nutrient solution equipment, so that the nutrient solution equipment can output the set nutrient solution into the culture container, so that the microalgae in the culture container have the benefit of accelerating or slowing down the growth, so that the microalgae culture can reach the scale Efficiency of transformation.

Description

Smart Microalgae Cultivation System

This creation relates to an intelligent microalgae cultivation system, especially one that has the benefit of accelerating or slowing down growth, so that microalgae cultivation can achieve large-scale efficiency, and is suitable for the microalgae cultivation industry or similar cultivation environments.

Among the greenhouse gases emitted by industry, carbon dioxide is the largest. Because of the high photosynthesis efficiency and rapid growth of microalgae, the carbon dioxide reduction efficiency of microalgae cultivation is more than ten times that of ordinary plants. Carbon dioxide reduction through the use of biotechnology and engineering technology to cultivate microalgae, especially the direct use of carbon dioxide-containing industrial waste gas to cultivate algae to reduce carbon dioxide, is worth developing.

Microalgae material can be used for the production of various biofuels such as biodiesel, bioethanol, hydrogen, coke, etc., and microalgae can convert carbon dioxide into sugars, proteins, lipids and other cellular components through photosynthesis. Therefore, algae species that can simultaneously produce useful substances such as physiologically active substances, pigments such as lutein and carotenoids, and omega-3 fatty acids such as EPA and DHA during carbon fixation are the most economical. In addition, microalgae can be used as animal or aquaculture feed, as well as for the treatment of wastewater and waste gas.

However, most of the microalgae cultivation with carbon reduction as the main goal adopts the self-supporting growth method, and the main control conditions required for its growth are light, temperature, carbon dioxide, and nutrients in the medium. Many of these conditions are environmental factors, so most use the design of microalgae culture systems to improve the efficiency of microalgae cultivation. At present, there are two ways of large-scale artificial cultivation of microalgae: open pond and (semi) closed reactor, the latter is called photobioreactor because it can provide sufficient light. In addition, the use of photobioreactor to cultivate microalgae can achieve higher algal cell density (high yield of microalgae), because the carbon dioxide introduced into the reactor has a longer residence time in the culture liquid, and can fully conduct gas-liquid Therefore, the efficiency of dissolving carbon dioxide in the culture medium is high, and the effect of carbon reduction is also better, but the cost of construction and operation is relatively high. Large-scale farming is not feasible if the microalgae produced cannot be of high economic value.

Therefore, in view of the above deficiencies, the author hopes to propose an intelligent microalgae cultivation system that enables microalgae cultivation to achieve large-scale efficiency, so that users can easily complete the operation and installation. , in order to provide the convenience of users and the creators of the creative motivation that the creators want to develop.

The main purpose of this creation is to provide an intelligent microalgae cultivation system, which includes a microalgae cultivation farm, a nutrient solution equipment, at least one monitoring equipment and a control computer system, mainly through the installation in the control computer system. The inner convolutional neural network (CNN) identifies the image of at least one culture container recorded by the monitoring equipment, and identifies the microalgae concentration of at least one culture container, and then the control computer system identifies the at least one culture container according to the identification of the microalgae concentration. The nutrient solution equipment can be operated by changing the concentration of microalgae in the container, so that the nutrient solution equipment can output the set nutrient solution into the culture container, so that the microalgae in the culture container have the benefit of accelerating or slowing down the growth, so that the microalgae in the culture container can accelerate or slow down the growth. Algae cultivation can achieve large-scale efficiency, thereby increasing the overall practicability.

Another object of this creation is to provide an intelligent microalgae cultivation system, through which the control computer contains a machine learning program, when the convolutional neural network (CNN) After identifying the image of the culture vessel recorded by the monitoring equipment into the concentration of microalgae, each time the identified as the concentration of microalgae is integrated and data analysis is performed, and a machine learning (machine learning) program is used to establish a The microalgae cultivation model, and the feedback control structure is formed by the microalgae cultivation model, so as to achieve the efficiency of building an intelligent microalgae cultivation system, thereby increasing the overall constructability.

In order to further understand the features, characteristics and technical content of this creation, please refer to the following detailed descriptions and accompanying drawings of this creation, but the attached drawings are only for reference and description, and are not intended to limit this creation.

10: Microalgae Farm

11: Shelving

12: Culture container

121: Microalgae concentration

20: Nutrient solution equipment

30: Monitoring equipment

31: Lens

32: Video

40: Control computer system

50: Position Sensor

Figure 1 is the first schematic diagram of the microalgae cultivation system created by the author.

Figure 2 is the second schematic diagram of the microalgae cultivation system created by the author.

Please refer to Figures 1 to 2, which are schematic diagrams of the creative embodiment, and the best embodiment of the intelligent microalgae cultivation system and method of the present invention is suitable for the microalgae cultivation industry or similar cultivation environment, and has the advantages of The benefits of accelerating growth or slowing down growth allow microalgae farming to achieve large-scale efficiency.

The intelligent microalgae cultivation system of the present creation mainly includes a microalgae cultivation farm 10, a nutrient solution equipment 20, at least one monitoring equipment 30 and a control computer system 40 (as shown in Figures 1 and 2) , the microalgae farm 10 is provided with a plurality of scaffolds 11 and at least one culture container 12, and the culture container 12 is placed on the scaffold 11 (as shown in FIG. 1), wherein the culture container 12 is The culture medium and microalgae are put in, and the culture container 12 and the culture container 12 are connected with each other, so that the culture medium and the microalgae located in the culture container 12 can be stored in the culture container 12. The culture containers 12 flow between each other, and the culture containers 12 are all transparent, so as to increase the transparency of light irradiation during microalgae cultivation.

The above-mentioned microalgae farm 10 is provided with a nutrient solution device 20 and at least one monitoring device 30 (as shown in FIG. 1 and FIG. 2 ), and the nutrient solution device 20 and the monitoring device 30 are respectively connected with the control device 30 . The computer system 40 is connected, wherein the nutrient solution equipment 20 is connected with the culture container 12, so that the nutrient solution is transported into the culture container 12 through the nutrient solution equipment 20 (as shown in FIG. 2), so that the culture container 12 The microalgae inside can obtain nutrient solution, which is convenient for increasing growth. In addition, the monitoring device 30 is provided with a lens 31, and through the lens 31, an image 32 of at least one culture container 12 is recorded (as shown in FIG. 1), and then The image 32 of at least one culture vessel 12 is transmitted to the control computer system 40 for storage.

In addition, the above-mentioned culture container 12 is equipped with a position sensor 50 , the position sensor 50 is installed at the culture container 12 (as shown in FIG. 1 ), and the position sensor 50 is connected with the position sensor 50 . The control computer system 40 is connected so that the control computer system 40 can know the position where the culture container 12 is placed according to the position sensor 50 , so that the control computer system 40 can perform subsequent operations.

Furthermore, the control computer system 40 is equipped with a convolutional neural network (Convolutional Neural Networks, CNN), the convolutional neural network (CNN) is a two-dimensional convolutional neural network (not shown), to Image recognition is performed through the two-dimensional convolutional neural network, and the convolutional neural network (CNN) is mainly used to identify two-dimensional graphics with displacement, scaling and other forms of distortion invariance. This part of the function is mainly realized by the pooling layer , and the basic structure of the convolutional neural network (CNN) includes two layers, one of which is a feature extraction layer, the input of each neuron is connected to the regional receptive field of the previous layer, and the regional features are extracted. Once the regional feature is extracted, it is combined with other features The positional relationship between the features is also determined; the second is the feature mapping layer, each computing layer of the network consists of multiple feature mappings, each feature mapping is a plane, and the weights of all neurons on the plane are value is equal.

The above-mentioned control computer system 40 uses a convolutional neural network (CNN) to identify the image 32 of at least one culture container 12 recorded by the monitoring device 30 (as shown in FIG. 1 ), when the culture container 12 After the microalgae inside grow for a period of time, the microalgae concentration 121 (as shown in FIG. 1 ) of the culture container 12 is recorded by the convolutional neural network (CNN), and the microalgae concentration 121 of the culture container 12 is recorded. The algae concentration 121, and when the microalgae concentration 121 of the culture container 12 is different from the set target value (as shown in FIG. 2), the control computer system 40 identifies the microalgae concentration 121 of the culture container 12 according to the The nutrient solution device 20 can be operated according to the change of the nutrient solution device 20, so that the nutrient solution device 20 can output the set nutrient solution into the culture container 12 (as shown in FIG. 2), so that the microalgae in the culture container 12 can accelerate the growth or The benefit of slowing growth. In addition, the control computer system 40 includes a machine learning program (not shown), when the convolutional neural network (CNN) recognizes the image 32 of the culture container 12 recorded by the monitoring device 30 as microscopic After the algae concentration 121, each time the identified microalgae concentration 121 is integrated and data analysis is performed, and a microalgae cultivation model is established through the machine learning program, and the feedback control is formed by the microalgae cultivation model. structure to achieve the efficiency of building an intelligent microalgae farming system, thereby increasing the overall constructability.

From the above detailed description, those skilled in the art can understand that this creation can indeed achieve the above-mentioned purpose, which is in compliance with the provisions of the Patent Law, and can therefore file a patent application for the creation.

However, the above are only the preferred embodiments of this creation, and should not limit the scope of implementation of this creation; therefore, any simple equivalent changes and modifications made according to the scope of the patent application for this creation and the content of the creation description , shall still fall within the scope of this creative patent.

10: Microalgae Farm

11: Shelving

12: Culture container

121: Microalgae concentration

30: Monitoring equipment

31: Lens

32: Video

40: Control computer system

Claims (5)

An intelligent microalgae cultivation system includes: a microalgae farm, the microalgae farm is equipped with at least one culture container, and the culture container is filled with the culture solution and the microalgae; a nutrient solution device, the nutrient solution device is installed in the microalgae farm, and the nutrient solution device is connected with the at least one culture container; at least one monitoring device, the monitoring device is installed in the microalgae farm, and the monitoring device records images of at least one culture container through a lens; and A control computer system, the control computer system is respectively connected with the monitoring equipment and the nutrient solution equipment, and the control computer system is equipped with a convolutional neural network (CNN) to pass the convolutional neural network (CNN) Identify the image of at least one culture vessel recorded by the monitoring equipment, and identify the concentration of microalgae in the at least one culture vessel, and then operate the control computer system according to the change of the concentration of microalgae in the identified at least one culture vessel The nutrient solution device enables the nutrient solution device to output the set nutrient solution into the culture container. The intelligent microalgae cultivation system as described in item 1 of the patent application scope, wherein the convolutional neural network (CNN) is further a two-dimensional convolutional neural network, so as to perform imaging through the two-dimensional convolutional neural network identify. The intelligent microalgae cultivation system as described in claim 1, wherein at least one cultivation container of the microalgae cultivation farm is further equipped with at least one position sensor, and the position sensor is installed in the at the culture container. The intelligent microalgae cultivation system as described in claim 3, wherein the position sensor is further connected with the control computer system. The intelligent microalgae cultivation system as described in item 1 of the patent application scope, wherein the control computer system further includes a machine learning program, and the convolutional neural network (CNN) records the images of the monitoring equipment. The image of at least one culture container is identified as the concentration of microalgae for data analysis, so as to establish a microalgae cultivation model.

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