AI-Based Waste Assessment and Product Recommendation Decision System

Abstract

The invention relates to an AI-powered decision support system (100) that enables the identification and analysis of waste materials and their conversion into value-added products using appropriate production methods. The system (100) includes a data input module (10) that allows the inclusion of visual, categorical, or textual data on the waste material into the system via a user interface (11). The raw data received is processed by an AI analysis module (20) that determines the type of material, its physical properties, and its usability in production processes through image processing and machine learning algorithms. The identified characteristics are cross-matched with the database (31) within the system by the database query module (30), and theoretical production methods are filtered.Based on this filtered data, a product recommendation algorithm (40) is run that calculates alternative value-added products that can be produced from waste and the list of additional equipment / materials that will be needed in this process. The resulting final production recipes and product recommendations are reported through a results and user presentation module (50), thus completing the cycle.

Description

1 TARIFF AI-Based Waste Valorization and Product Recommendation Decision System TECHNICAL FIELD The invention involves AI-powered decision support systems, image processing, computer vision, 5 What to learn: waste management, recycling technologies, and circular economy. It relates to these fields. More specifically, invention, computer-based systems, and artificial intelligence. The field of intelligence is related to lg ld r. STATE OF THE ART 10 Different types of technologies are used in the field of waste management and recycling. Current systems generally involve the classification, separation, and recycling of waste. It offers solutions for managing transformation processes, especially the latest ones. In recent years, waste has been processed automatically using artificial intelligence and image processing technologies. Systems have been developed that enable them to be recognized and differentiated. These 15 Systems are mostly found in recycling facilities or industrial plants. camera-based image processing applications or smart waste sorting used They appear in the form of systems. In addition, within the framework of the circular economy approach, waste is recycled... Various databases and digital platforms are used for evaluation purposes. However, current solutions generally only involve recycling waste. The focus is on directing the process and adding value to waste materials. Comprehensive decision support that offers production recommendations aimed at transforming products into tangible results. Systems are quite limited. Some studies in the field of technology suggest AI-assisted solutions. While systems or sustainable production databases exist, these systems It is mostly limited to a specific sector or type of material. The current technologies used in waste management and recycling are mostly... What are the processes of classifying, separating, and recycling waste? It focuses on the orientation. Image processing or sensor-based systems 30 Various solutions have been developed to identify waste types through this method. However, these systems generally only categorize waste into the recycling category. 2 It focuses on identifying and transforming materials into value-added products. It does not conduct a comprehensive assessment. In current practices, waste materials are not utilized in production processes. Access to resources for waste management is quite limited. Which products can the waste be transformed into? which production methods can be used or the economic and 5 of this transformation Whether it is technically feasible or not mostly depends on expert knowledge. This is based on integrating waste into value-added production processes. This makes it more difficult to implement and creates many potential production opportunities. This leads to the inability to evaluate it. In addition, existing systems generally have limitations in terms of material type, production method, and user 10. numerous variables such as capacity and local production conditions together It does not have integrated decision support mechanisms that can be evaluated. This Therefore, waste needs to be analyzed, matched with appropriate production methods, and processed in different ways. Enables the development of viable production suggestions for user profiles. An integrated CBR technology solution is needed. 15 EXPLANATION OF THE INVENTION The main purpose of the invention is to capture the image of waste material provided by the user. using artificial intelligence techniques to analyze and automatically identify materials, the defined material's technical specifications and 20 for that material Integrated CBR decision that automatically generates feasible production options The goal is to develop a support system. The proposed invention uses waste materials not only in recycling processes. Instead of directing them, suggestions for transforming them into value-added products 25 It offers an integrated AI-based decision support system. This In this respect, the invention differs from existing technology in that it only classifies waste. or not limited to separation, but also in the production processes of these materials It is conducting a comprehensive analysis aimed at its evaluation. In this analysis The user is provided with information on the type of waste, cleaning methods, and the added value of recycling. valuable products and materials that need to be used in this process and what needs to be monitored The route map is explained. 3 One of the key advantages of the invention is the link between waste materials and potential products. By enabling systematic KBR matching, decisions are made regarding production processes. It offers a support mechanism. Thus, users can manage their waste. In which production areas are the materials suitable from a technical and economic perspective? 5 It is possible to obtain quick and data-driven recommendations on what can be evaluated. This system provides access to information on waste recycling. It is becoming easier, decision-making processes regarding production processes are accelerating, and It is becoming possible to use waste in value-added production activities. In these respects, the invention is more comprehensive, systematic and innovative than existing techniques. It offers a specific approach. Reference List 100. Artificial Intelligence-Powered Decision Support System 10. Data Entry Module 15 11. User Interface 20. Artificial Intelligence Analysis Module 30. Database Query Module 31. Database 40. Product Recommendation Algorithm 20 50. Results and User Presentation Module DETAILED DESCRIPTION OF THE INVENTION This detailed explanation describes the invention as an artificial intelligence-based waste reprocessing and product development process. The suggestion / decision-making system is related to the issue and is only aimed at a better understanding of the subject. It includes explanations and does not contain any restrictive elements whatsoever. The invention involves analyzing waste materials and developing added-value products that can be produced from these materials. An AI-based decision support system that enables the identification of valuable products. le lg ld r. S system; user interface, waste data entry module, artificial intelligence analysis 30 The module consists of database, product recommendation and roadmap recommendation algorithms. integrated kbr structure sah pt r. 4 User Interface and Waste Data Entry Module: The first component of a system is the user interface. The user accesses the system through this interface. Each individual can enter data regarding the waste material they own into the system. This data... The process involves uploading a photo of the waste, selecting the type of waste, or selecting the material. The relationship can be carried out by entering the basic information manually. User 5 The data entered into the system by the system is received and analyzed by the waste data entry module. What is the process being transferred to? Data entry module (10); external artificial intelligence supported decision support system (100) It is the first operational layer that directly interacts with the world and the user. This 10 The module's basic technical function is related to the waste material targeted for transformation. Collection of unstructured raw data (visual, textual, or categorical), compilation and system (100) can be processed in standard brdj tal format conversion is done. Data entry module (10), user interaction within itself. It is done through the user interface (11) it contains. User interface (11); 15 mobile application, web-based portal or integrated industrial display form The configurable lrn tel kted r. Ver ed nm process, via user interface (11) nden synchronized three different methods that can be carried out either on time or independently is being carried out:  Visual Data Upload: The user can upload real-time images of their waste materials. by taking photos or visual data stored on the device, the user It uploads the visual data to the system via the interface (11). These visual data include the geometry of the waste, Volume, color, and surface texture are fundamental inputs for determining GBFZ ksel properties. n carries the letter ğ.  Category Selection: User interface (11), predefined waste in the system 25 their classes (e.g., polymers, metals, organic waste, composite materials) etc.) offers drop-down menus or selection screens. The user can use these screens You can categorize the type of waste on the system.  Manual Data Entry: To support visual or categorical data, The approximate weight of the waste, estimated dimensions, or chemical composition if known. 30 GB digital and textual data, text located in the user interface (11) They can be manually placed in their boxes. Data entry module (10); all these visuals collected via the user interface (11), Category and textual parameters are synchronized into a single data packet. The collected raw data are processed through the preprocessing algorithms within module (10). DJ TAL BR formata converted to remove noise (invalid or erroneous data inputs) It is purified. This pre-processed and standardized data set is characterization 5. and the next component of the system (100) so that the analysis processes can be started. The data is transferred to the artificial intelligence analysis module (20) in a precise manner. Artificial Intelligence Analysis Module (20): Waste data uploaded to the system by the user The artificial intelligence analysis module processes the data. This module handles image processing, Using algorithms to learn or analyze the type of waste material, 10 and determines its basic characteristics. At this stage, the system determines the type of material of the waste, structural characteristics, effects on human health, and production processes It evaluates its usability. Database Query Module (30): Obtained by the artificial intelligence analysis module The data obtained is compared with the database in the system. This database has 15 It includes different waste materials, the physical properties of these materials, and their applicability. The system contains information about production methods and potential product types. By matching the analyzed material properties with the information in the database, the appropriate solution is determined. It determines the production options. Product Recommendation Algorithm (40): 20 obtained as a result of matching with the database The information is evaluated by the product recommendation algorithm. This algorithm assesses the material's... Analysis of criteria such as characteristics, production method options, and production feasibility. by developing value-added product alternatives that can be produced from waste materials. The system is determining this. At this stage, the system suggests one or more products. It can be created. Also, the use of this product is needed in the production process. 25 Other materials and equipment that will be detected will also be notified by the system. Results and User Presentation Module (50): By the product recommendation algorithm The determined results are presented to the user via the system interface. The user At this stage, which products can be transformed from the waste material that is owned? You can view suggestions and information about related production methods. It is possible to obtain it. 6 Thanks to this system, waste materials can be analyzed and suitable production methods can be used. Matching and developing value-added product proposals on a single digital platform. This can be done through this method. Thus, waste materials in production processes This facilitates the evaluation and promotes sustainable production approaches. It is intended to provide support. 5 S system n working principle b The artificial intelligence-supported decision support system (100) that is the subject of the invention; waste materials n Identification, analysis and transformation of these wastes into value-added products 10 Closed-loop microcontroller that enables the execution of the necessary production algorithms. The mechanism is the system (100) functioning, from the data entry to the product recommendation. It consists of sequential operations of modules that work together in an integrated manner. The system's (100) working cycle refers to the waste material to be converted. and by including the descriptive inputs into the system via the input module (10) 15 starts. The user interface (11) which is a component of the data entry module (10) Through this method, you upload visual data (photos) about the waste, select the type of waste, or Basic information about the material is entered manually. Data entry module (10) This collected data is sent to the artificial intelligence analysis module in digital form for analysis. (20) is transferred. 20 The artificial intelligence analysis module (20) processes the waste data transferred to it, The machine learns or analyzes data using algorithms. At this stage... artificial intelligence analysis module (20); waste material type, structural and physical its properties, possible effects on human health, and industrial production. It autonomously identifies and characterizes the usability potential in processes. 25 These characteristic data obtained by the artificial intelligence analysis module (20) are data Let l r. Database query module (30) let l r. Database query module (30), incoming with the information in the relational database (31) located within the data system compares. The database in question (31) compares different waste materials, these materials n physical / chemical properties, applicable production methods and potential product 30 It includes types. Database query module (30), analyzed material By cross-matching the features with the parameters in the database (31), the most suitable theory is found. Production options, filters. 7 The refined data set obtained as a result of matching and filtering, product recommendations. The product recommendation algorithm (40) is transferred to the product recommendation algorithm (40); material properties, alternative production methods and feasibility of on-site production are some of the criteria. By analyzing waste materials holistically, value-added products can be produced from them. The alternatives are determined. The product recommendation algorithm (40) at this stage one or more 5 When creating a product proposal, we also need to consider the main waste generated during the production process of this product. other auxiliary materials and technical equipment that will be needed in the field are also listed. accounts. All products optimized and finalized by the product recommendation algorithm (40) The results are presented to the results and user presentation module (50). Results and user presentation 10 presentation module (50), product recommendations obtained, necessary materials and equipment The prescriptions are presented to the user visually via the user interface (11) and s stem (100) completes the waste transformation cycle feeding. 20 30

Claims

8 REQUESTS 1. The invention involves analyzing waste materials and transforming them into value-added products. an AI-powered decision support system that enables transformation (100) and its feature is; 5  The user provides visual or textual data about the waste material to the system. a data entry module that enables g rmes n (10),  The data received from the said data entry module (10) is image processing And by processing the machine's learning algorithms, it transforms waste material. type, structural characteristics, effects on human health, and production 10 an artificial intelligence analysis module that determines usability in processes (20),  Material obtained by the artificial intelligence analysis module (20) Its characteristics include different waste materials, physical properties, Applicable production methods and potential product types are found in br 15 compare with the database (31) suitable production options Matching database query module (30),  As a result of this matching, the material properties and production Analysis of method options and production feasibility criteria. by producing value-added products from waste materials 20 alternatives, other materials that will be needed for the production of these products A product recommendation algorithm that determines the materials and equipment along with the product. (40) The character of the chermes is damaged. According to claim 1, the AI-supported decision support system is (100), feature; product recommendations and needs determined by the algorithm (40) 25 The materials heard are conveyed to the user via the interface (11) as a result and The presentation module (50) is characterized by its user interface. According to claim 1, the AI-supported decision support system is (100), feature; the mentioned data entry module (10), the user’s visual of the waste 30 (This section appears to be a list of unrelated words and phrases and should not be translated as it is not part of the translation.) The user interface (11) that provides the character is zed r. According to claim 1, the AI-supported decision support system is (100), The feature of the mentioned artificial intelligence analysis module (20) is loaded into the system. 9 Waste visual material through object recognition and image processing. It is characterized by its ability to autonomously determine its form and type. According to claim 1, the AI-supported decision support system is (100), The feature of the mentioned artificial intelligence analysis module (20) is that it was detected. The material analyzes potential risk factors to human health. 5 le character zed r. According to claim 1, the AI-supported decision support system is (100), The feature of the mentioned product recommendation algorithm (40) is the added value suggested by the system. using the main waste material in the production process of the valuable product 10 character zed r. According to claim 1, the AI-supported decision support system is (100), feature of the mentioned database query module (30), waste materials by cross-matching the potential end product catalogs, theoretical production The methods are characterized by their filtering and related structure. 15 8. The invention involves analyzing waste materials and transforming them into value-added products. It is an AI-powered method that enables transformation, and its feature is;  Visuals related to waste material via the data entry module (10), categorical or textual data are not included in the system,  The data received into the system by the artificial intelligence analysis module (20) image processing, machine learning, or data analysis algorithms by processing the waste material type, structural and physical properties, potential effects on human health and industrial production autonomous determination of usability potential in processes and character development, 25  By the database query module (30), the character is ze ed len The different waste materials included in the database (31), physical / chemical properties and applicable production methods by cross-matching the most suitable theoretical generation options filtering, 30  Product recommendation algorithm (40) determines the characteristics of the waste material, Production method options and feasibility of production in the field By analyzing the criteria holistically, from waste material the production of value-added product alternatives and this transformation Auxiliary materials and technical equipment that will be needed during the process determination of the list,  Optimized by the results and user presentation module (50) Product recommendations and production recipes user interface (11) 5 presented to the user through visualization, The process steps are characterized by their inclusion.

9. According to claim 8, this is a method whose characteristic is that the data input is the user's waste. This is accomplished by uploading a photo or manually providing information. character zed r. 10 10. According to claim 8, this is a method whose characteristic is that the analysis step involves the human element in the waste. including profiling of potential risk factors related to health character zed r.

11. This is a method according to claim 8, and its characteristic is that the product recommendation step involves waste material. Prescription 15 containing the auxiliary components that should be used in the harc. The character is damaged by its creation. 25