Search

DNA computer based algorithm is developed and evaluated for recyclable waste paper segregation.The concepts of replication and massive parallelism operations are used.The matching stage consists of Copy, Extract, Detect and Merge or Union operations.Gel electrophoresis operation is used to identify the candidate paper object grade.Success rate are 92% for WP, 90% for ONP and 93% for OCC with template size 5i?5. This article explores the application of DNA computing in recyclable waste paper sorting. The primary challenge in paper recycling is to obtain raw materials with the highest purity. In recycling, waste papers are segregated according to their various grades, and these are subjected to different recycling processes. Highly sorted paper streams facilitate high quality end products, while saving on processing chemicals and energy. In the industry, different sensors are used in paper sorting systems, namely, ultrasonic, lignin, gloss, stiffness, infra-red, mid-infra red, and color sensors. Different mechanical and optical paper sorting systems have been developed based on the different sensors. However, due to inadequate throughput and some major drawbacks related to mechanical paper sorting systems, the popularity of optical paper sorting systems has increased. The automated paper sorting systems offer significant advantages over the manual systems in terms of human fatigue, throughput, speed, and accuracy. This research has two objectives: (1) to use a web camera as an image sensor for the vision system in lieu of different sensors; and (2) to develop a new DNA computing algorithm based on the theme of template matching techniques for segregating recyclable waste papers according to paper grades. Using the concepts of replication and massive parallelism operations, the DNA computing algorithm can efficiently reduce the computational time of the template matching method. This is the main strength of the DNA computing algorithm in actual inspections. The algorithm is implemented by using a silicon-based computer to verify the success rate in paper grade identification.

This article explores the application of image processing techniques in recyclable waste paper sorting. In recycling, waste papers are segregated into various grades as they are subjected to different recycling processes. Highly sorted paper streams facilitate high quality end products and save processing chemicals and energy. From 1932 to 2009, different mechanical and optical paper sorting methods have been developed to fill the paper sorting demand. Still, in many countries including Malaysia, waste papers are sorted into different grades using a manual sorting system. Because of inadequate throughput and some major drawbacks of mechanical paper sorting systems, the popularity of optical paper sorting systems has increased. Automated paper sorting systems offer significant advantages over human inspection in terms of worker fatigue, throughput, speed, and accuracy. This research attempts to develop a smart vision sensing system that is able to separate the different grades of paper using first-order features. To construct a template database, a statistical approach with intra-class and inter-class variation techniques are applied to the feature selection process. Finally, the K-nearest neighbor (KNN) algorithm is applied for paper object grade identification. The remarkable achievement obtained with the method is the accurate identification and dynamic sorting of all grades of papers using simple image processing techniques.

Automated sorting for plastic recyclables has been seen as the way forward in the plastic recycling industry. Automated sorting provides significant improvements in terms of efficiency and consistency in the sorting process. In the case of macro sorting, which is the most common type of automated sorting, efficiency is determined by the mechanical details of the material handling system as well as the detection system. This paper provides a review on the state of-the-art technologies that have been deployed by some of the recycling facilities abroad. The design and development of a cost effective prototype automated system for sorting plastic recyclables is proposed and discussed.

Although template matching method is widely used for object identification, the large computational time is the major drawback to use this method in real time application. Using the concepts of replication and massive parallelism operations, the DNA computing algorithm can efficiently reduce the computational time of the template matching method. The emphasis of this research has been given in two objectives, namely development of a generic DNA computing algorithm for object identification based on the theme of the template matching technique and application of this algorithm for recyclable waste paper sorting. The achieved classification success rates are 92%, 90%, and 93% with template size 5 × 5 pixels for white paper, old newsprint paper and old corrugated cardboard, respectively.

This paper explores the application of image processing techniques in recyclable waste paper sorting. In recycling, waste papers are segregated into various grades as they are subjected to different recycling processes. Highly sorted paper streams will facilitate high quality end products, and save processing chemicals and energy. Since 1932 to 2009, different mechanical and optical paper sorting methods have been developed to fill the demand of paper sorting. Still, in many countries including Malaysia, waste papers are sorted into different grades using manual sorting system. Due to inadequate throughput and some major drawbacks of mechanical paper sorting systems, the popularity of optical paper sorting systems is increased. Automated paper sorting systems offer significant advantages over human inspection in terms of fatigue, throughput, speed, and accuracy. This research attempts to develop a smart vision sensing system that able to separate the different grades of paper using Template Matching. For constructing template database, the RGB components of the pixel values are used to construct RGBString for template images. Finally, paper object grade is identified based on the maximum occurrence of a specific template image in the search image. The outcomes from the experiment in classification for White Paper, Old Newsprint Paper and Old Corrugated Cardboard are 96%, 92% and 96%, respectively. The remarkable achievement obtained with the method is the accurate identification and dynamic sorting of all grades of papers using simple image processing techniques.