Feature extraction using Hough transform for solid waste bin level detection and classification

This paper deals with the solid waste image detection and classification to detect and classify the solid waste bin level. To do so, Hough transform techniques is used for feature extraction to identify the line detection based on image’s gradient field. The feedforward neural network (FFNN) model is used to classify the level content of solid waste based on learning concept. Numbers of training have been performed using FFNN to learn and match the targets of the testing images to compute the sum squared error with the performance goal met. The images for each class are used as input samples for classification. Result from the neural network and the rules decision are used to build the receiver operating characteristic (ROC) graph. Decision graph shows the performance of the system waste system based on area under curve (AUC), WS-class reached 0.9875 for excellent result and WS-grade reached 0.8293 for good result. The system has been successfully designated with the motivation of solid waste bin monitoring system that can applied to a wide variety of local municipal authorities system.     

Assessment of methane emission and oxidation at Air Hitam Landfill site cover soil in wet tropical climate

Methane (CH₄) emissions and oxidation were measured at the Air Hitam sanitary landfill in Malaysia and were modeled using the Intergovernmental Panel on Climate Change waste model to estimate the CH₄ generation rate constant, k. The emissions were measured at several locations using a fabricated static flux chamber. A combination of gas concentrations in soil profiles and surface CH₄ and carbon dioxide (CO₂) emissions at four monitoring locations were used to estimate the CH₄ oxidation capacity. The temporal variations in CH₄ and CO₂ emissions were also investigated in this study. Geospatial means using point kriging and inverse distance weight (IDW), as well as arithmetic and geometric means, were used to estimate total CH₄ emissions. The point kriging, IDW, and arithmetic means were almost identical and were two times higher than the geometric mean. The CH₄ emission geospatial means estimated using the kriging and IDW methods were 30.81 and 30.49 g m^?2 day^?1, respectively. The total CH₄ emissions from the studied area were 53.8 kg day^?1. The mean of the CH₄ oxidation capacity was 27.5 %. The estimated value of k is 0.138 year^?1. Special consideration must be given to the CH₄ oxidation in the wet tropical climate for enhancing CH₄ emission reduction.     

Physico‐mechanical and Flammability Properties of Cyrtostachys renda Fibers Reinforced Phenolic Resin Bio-composites

Journal of Polymers and the Environment - The aim of this study is to investigate the effect of fiber length and loading on physico-mechanical and flammability properties of Cyrtostachys renda (CR)...     

Radio Frequency Identification (RFID) and communication technologies for solid waste bin and truck monitoring system

This paper deals with a system of integration of Radio Frequency Identification (RFID) and communication technologies for solid waste bin and truck monitoring system. RFID, GPS, GPRS and GIS along with camera technologies have been integrated and developed the bin and truck intelligent monitoring system. A new kind of integrated theoretical framework, hardware architecture and interface algorithm has been introduced between the technologies for the successful implementation of the proposed system. In this system, bin and truck database have been developed such a way that the information of bin and truck ID, date and time of waste collection, bin status, amount of waste and bin and truck GPS coordinates etc. are complied and stored for monitoring and management activities. The results showed that the real-time image processing, histogram analysis, waste estimation and other bin information have been displayed in the GUI of the monitoring system. The real-time test and experimental results showed that the performance of the developed system was stable and satisfied the monitoring system with high practicability and validity.     

Solid waste bin detection and classification using Dynamic Time Warping and MLP classifier

The increasing requirement for Solid Waste Management (SWM) has become a significant challenge for municipal authorities. A number of integrated systems and methods have introduced to overcome this challenge. Many researchers have aimed to develop an ideal SWM system, including approaches involving software-based routing, Geographic Information Systems (GIS), Radio-frequency Identification (RFID), or sensor intelligent bins. Image processing solutions for the Solid Waste (SW) collection have also been developed; however, during capturing the bin image, it is challenging to position the camera for getting a bin area centralized image. As yet, there is no ideal system which can correctly estimate the amount of SW. This paper briefly discusses an efficient image processing solution to overcome these problems. Dynamic Time Warping (DTW) was used for detecting and cropping the bin area and Gabor wavelet (GW) was introduced for feature extraction of the waste bin image. Image features were used to train the classifier. A Multi-Layer Perceptron (MLP) classifier was used to classify the waste bin level and estimate the amount of waste inside the bin. The area under the Receiver Operating Characteristic (ROC) curves was used to statistically evaluate classifier performance. The results of this developed system are comparable to previous image processing based system. The system demonstration using DTW with GW for feature extraction and an MLP classifier led to promising results with respect to the accuracy of waste level estimation (98.50%). The application can be used to optimize the routing of waste collection based on the estimated bin level.     

Adsorptive removal of Cd(II) and Pb(II) ions from aqueous solutions by using Turkish illitic clay

The ability of Turkish illitic clay (TIC) in removal of Cd(II) and Pb(II) ions from aqueous solutions has been examined in a batch adsorption process with respect to several experimental conditions including initial solution pH, contact time, initial metal ions concentration, temperature, ionic strength, and TIC concentration, etc. The characterization of TIC was performed by using FTIR, XRD and XRF techniques. The maximum uptake of Cd(II) (11.25 mg g⁻¹) and Pb(II) (238.98 mg g⁻¹) was observed when used 1.0 g L⁻¹ of TIC suspension, 50 mg L⁻¹ of initial Cd(II) and 250 mg L⁻¹ of initial Pb(II) concentration at initial pH 4.0 and contact time of 240 min at room temperature. The experimental data were analyzed by the Langmuir, Freundlich, Temkin and Dubinin Radushkevich (D-R) isotherm models. The monolayer adsorption capacity of TIC was found to be 13.09 mg g⁻¹ and 53.76 mg g⁻¹ for Cd(II) and Pb(II) ions, respectively. The kinetics of the adsorption was tested using pseudo-first-order, pseudo-second-order, Elovich and intraparticle diffusion models. The results showed that the adsorption of Cd(II) and Pb(II) ions onto TIC proceeds according to the pseudo-second-order model. Thermodynamic parameters including the Gibbs free energy (ΔG), enthalpy (ΔH), and entropy (ΔS) changes indicated that the present adsorption process was feasible, spontaneous and endothermic in the temperature range of 5–40 °C.     

Removal of copper from water using limestone filtration technique. Determination of mechanism of removal

This paper discusses heavy metal removal from wastewater by batch study and filtration technique through low-cost coarse media. Batch study has indicated that more than 90% copper (Cu) with concentration up to 50 mg/l could be removed from the solution with limestone quantity above 20 ml (equivalent to 56 g), which indicates the importance of limestone media in the removal process. This indicates that the removal of Cu is influenced by the media and not solely by the pH. Batch experiments using limestone and activated carbon indicate that both limestone and activated carbon had similar metal-removal efficiency (about 95%). Results of the laboratory-scale filtration technique using limestone particles indicated that above 90% removal of Cu was achieved at retention time of 2.31 h, surface-loading rate of 4.07 m3/m2 per day and Cu loading of 0.02 kg/m3 per day. Analyses of the limestone media after filtration indicated that adsorption and absorption processes were among the mechanisms involved in the removal processes. This study indicated that limestone can be used as an alternative to replace activated carbon.     

An automated solid waste bin level detection system using a gray level aura matrix

An advanced image processing approach integrated with communication technologies and a camera for waste bin level detection has been presented. The proposed system is developed to address environmental concerns associated with waste bins and the variety of waste being disposed in them. A gray level aura matrix (GLAM) approach is proposed to extract the bin image texture. GLAM parameters, such as neighboring systems, are investigated to determine their optimal values. To evaluate the performance of the system, the extracted image is trained and tested using multi-layer perceptions (MLPs) and K-nearest neighbor (KNN) classifiers. The results have shown that the accuracy of bin level classification reach acceptable performance levels for class and grade classification with rates of 98.98% and 90.19% using the MLP classifier and 96.91% and 89.14% using the KNN classifier, respectively. The results demonstrated that the system performance is robust and can be applied to a variety of waste and waste bin level detection under various conditions.