Waste Management 29 (2009) 1467–1472
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Waste Management journal homepage: www.elsevier.com/locate/wasman
Sensorized waste collection container for content estimation and collection optimization F. Vicentini a, A. Giusti a,*, A. Rovetta a, X. Fan b, Q. He b, M. Zhu b, B. Liu b a b
Laboratory of Robotics, Politecnico di Milano, Via La Masa 1, 20133, Milan, Italy Computer Integrated Manufacturing Research Institute, Shanghai Jiao Tong University, Shanghai 200030, China
a r t i c l e
i n f o
Article history: Accepted 21 October 2008 Available online 21 December 2008
a b s t r a c t The concurrent effects of a fast national growth rate, of a large and dense residential area and a pressing demand for urban environmental protection create a challenging framework for waste management in Pudong New Area, Shanghai. The complexity of context and procedures is indeed a primary concern of local municipal authorities due to problems related to the collection, transportation and processing of residential solid waste. In order to design and implement a suitable urban solid waste system, the first task is to forecast the quantity and variance of solid waste as it relates to residential population, consumer index, season, etc. The system here discussed addresses exactly these issues, by means of an intelligent, sensorized container. The container has been prepared and tested in the Pudong New Area, Shanghai. Ó 2008 Elsevier Ltd. All rights reserved.
1. Introduction The concurrent effects of a fast national growth rate, of a large and dense residential area and a pressing demand for urban environmental protection, create a challenging framework for waste management in Pudong New Area, Shanghai (Rotich et al., 2006; The World Bank, 2005). The complexity of context and procedures is indeed a primary concern of local municipal authorities due to problems related to the collection, transportation and processing of residential solid waste. Under the viewpoint of global attention to the protection of human environment (Tinmaz and Demir, 2005), the sustainable development demand and the improvement of the living standard are allowing people to expect greater efforts in safety and effectiveness of waste management (Ahluwalia and Nema, 2006). This scenario forces stakeholders and operators to critically investigate the issues of costs and cost-effectiveness, public health and environmental impact. In order to design and implement a suitable urban solid waste system, the first task is to forecast the quantity and variance of solid waste as it relates to residential population, consumer index, season, etc. Then the major effort is focused on optimizing the schedule and routing of transportation trucks considering cost, waste weight and volume, distances, road condition, etc. (Lunkapis, 2004; Ogra, 2003). In recent years, the local authorities of Pudong, Shanghai have placed significant importance on the solid waste problem and made relevant achievements, but there is still a need for a more efficient waste disposal system to cope with the com* Corresponding author. Tel.: +39 02 2399 8453; fax: +39 02 2399 8492. E-mail address:
[email protected] (A. Giusti). 0956-053X/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.wasman.2008.10.017
plexity of urban solid waste disposal. The solution proposed in this paper, as a result of the CleanWings project (Intelligent Supervision for Big Area Waste Disposal System, Project Number: C/II/S/07/ 025), is a new tool based upon distributed technology in order to acquire information from every waste collection point that afterwards can be used for the information flow related to solid waste production, collection and disposal. In this paper, the authors discuss and propose an innovative and economically/commercially viable solution for waste monitoring and handling. The CleanWings project’s system intelligence is based on sampling and monitoring the waste in its first phase, which is the early collection phase in containers on streets or outside every house and factory. The container represents the first module of the overall system, particularly devoted to an early collection of data since all garbage is detected and processed. The recorded data are used throughout the information flow towards the Control Centre in order to map, monitor and plan the waste collection activities (Solano et al., 2005), as shown in Fig. 1. 2. The site of Pudong New Area and the current waste management situation The system discussed in this paper has been tested under actual conditions in Shanghai. The test area, Pudong New Area (Shanghai), is one of China’s more economically active cities. It is the modern, eastern part of Shanghai. Eighteen years ago it was all pastures, but massive government investments helped it grow extremely fast into a modern city. Pudong is situated at the coast of East China Sea and along the bank of the Huangpu River. On April 18, 1990, the Chinese Government declared the development and opening
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Fig. 1. General concept and flow of information of the CleanWings project. The data from the sensorized trucks on the position of the trucks are combined and used by the control centre for optimizing the whole procedure.
up of Pudong, thus awakening this sleeping land and starting the development and construction there on a large scale. Through 18 years of development and construction, Pudong has undergone significant changes and has begun to take shape. In 1990 the population of Pudong was about 1.4 million. However, the population increased to 2.8 million in 2005 according to the census. As cities grow, land use becomes increasingly complex and the waste generated increases in volume and variety (Omuta, 1987). The daily quantity of solid waste generated in Pudong has increased from 2418 tons in 2004 to 2854 tons in 2005. In order to facilitate the statistics and the organisation, the municipal solid waste (MSW) is divided into three categories: urban waste, suburban waste and other waste, according to the zone where the waste is produced. In 2006, the amount of MSW generated in Pudong was about 3108 tons per day, which is almost one-fifth of the total amount produced in Shanghai. Furthermore, the calorific value of this urban waste is about 5080 kJ/kg (PSWAO, 2006), which has value as a fuel, so most of the waste is transported to the incineration plant. Based on the current population growth trends, the solid waste quantity generated in Pudong will keep on rising with the city’s development. It is also very important for a complete understanding of the waste management situation to know the usual composition of the waste produced. There is a large variation in the composition of the waste from different areas. Compared with other cities in developing countries, the MSW in Pudong has a high organic content and low calorific value. The main components are food residues (almost 50%), plastics (about 33%), fruit (7%), paper (4%),
textile, glass and wood. The waste has a heterogeneous composition, comprising of both degradable and non-degradable materials, and it is collected without any sorting. 3. Preliminary design – testing prototype Taking the above situation into consideration, the authors tried to find a viable solution to the problem of sorting and treating such inhomogeneous waste in such a large city area that is not using waste separation techniques. The solution here discussed is the CleanWings intelligent container and the software to support it. The fundamental element of the project’s system is the container. By using a set of carefully selected sensors that can be put in the container, an estimation can be made of the quality and quantity of the waste present in the container. This information can be afterwards used for optimizing the entire waste management process, but also for billing reasons. After a development and test phase for the system definition, a container prototype has been set-up in the Politecnico di Milano Labs with sensors for data acquisition and processing in order to satisfy the general requirements of the CleanWings project in laboratory conditions. During the development phase, different hardware configurations have been tested and accepted or rejected according to evaluation procedures based on sampling, feasibility, accuracy, electrical compatibility (power, voltage and interface), integration, assembly and robustness features. As a result of the development phase, a complete version of the container (testing prototype, TP) has been released and tested for all the functions
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and applications. The TP’s objective is to provide solid results for setting up the preliminary design review (PDR) and critical design review (CDR) processes. On the basis of those processes, the final operating prototype (OP) has been integrated, assembled and used for trials. Hence, the importance of the TP phase is fundamental for the following integration and application issues. A set of different sensors monitors the content of the container every time the container is opened and closed again. The system is resting the remainder of the time for energy saving reasons. In this way, the actual content of each single container can be estimated with a high degree of precision. Sensors are placed on the top and the bottom of the container. On the top, a small camera, coupled with an ultrasonic distance sensor, can provide information about the shape, the area and the height of the object. A set of three LED lights is also placed to illuminate the container. The object’s volume can then accurately be calculated. Regarding the camera, a low-resolution RGB camera with focus distance from 10 cm to 1 m is adequate for the scope of the system here proposed. The ultrasonic sensor has an accuracy of no more than 3–4 cm and is in this way permitting an adequate object’s volume calculation while keeping the cost low. The illumination LED lights are low consumption white light LEDs with a lens angle of 45°. On the bottom of the container, a low-cost system using strain gauges is placed in order to weigh the object. The weight and volume information can then be combined to define a medium density for the object and so some estimation can be made on the material of the object. The proposed system can generate a profile (density versus time) of the waste deposited in the container. The fact that the system is controlling the content of the container every time an object is thrown in it, permits it to create this profile and in practice to achieve better results, compared to a system that checks the waste only at the end (i.e., when the container is full). In addition to the above mentioned sensors, a second layer is placed in the bottom of the container, with a hole in it and with some inclination, permitting collection of all the liquid in the container in a specific position. In that position, a tube can collect the liquid and with help of a pressure sensor the height of the liquid can be defined. The strain gauges used are simple variable electrical resistors (their resistance varies with weight changes) connected to form a Wheatstone’s bridge. The pressure sensor used is able to measure pressures corresponding to liquid heights up to 90 cm (common 240-L containers are 90 cm high). Fig. 2 presents the final TP configuration. All considerations about electronic measurements have been made (Klaassen, 1996; O’Dell, 1991; Sangwine, 1994). The TP application runs on Matlab, calling and enabling the ports and interfaces to obtain samples and signals. The TP application initializes the device status and the interfaces every time the container is dumped. Afterwards it checks the default (empty) status to warn if something is left behind or if the cover is not properly closed. Then the system is ready to iterate the sampling of the content and analyze the waste thrown inside until the container is full. A control branch is used to check if the container has not been closed after being filled. Data records are iteratively and incrementally populated any time the container is filled. When the container is full, the data collected and computed is packed into an output file ready to be sent by wireless transmission. The system is then re-initialized and ready to continue. As a result of the filling of the container, the application incrementally records the content whenever it is dropped in until it detects that the container is full and sets a warning, available for the control centre to be aware of the status of the container. An overall report of the full container is finally computed and the file can be sent to the control centre providing all the partial and global parameters that are requested by the system’s requirements (weight, volume, density and water content).
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Fig. 2. Modules and sensors used by the CleanWings system and their position in the waste collection container.
4. Conclusions and considerations from TP to OP At the end of the TP phase, several aspects are taken into consideration for developing the final operating prototype (OP). Energy and robustness are key aspects for the system’s success. The container is not a well-protected area. Extreme temperature and humidity conditions can be reached. In addition to that, people and operators are used to treating the waste containers in a harsh way. Theft problems could also occur if the system or parts of it can be generally useful. These aspects are taken into consideration in the project and the robustness of the system. The OP is not an industrial product; it is still a prototype, but it is meant to endure actual container conditions on the streets of Pudong New Area. A special protective cover is positioned to protect the camera on the container’s lid. The only transparent part is the camera lens. The LED lights and the ultrasonic sensors are disguised well-enough but not totally covered, in order not to interfere with their functionality. The bottom sensors are placed in a double-bottom layer, so they are totally hidden; the control unit is also placed in this hidden layer. Energy consumption of the OP should at any case be low enough to permit the autonomous functionality of the system for at least the time needed for it to be filled. In order to obtain this objective, the system is not continuously working but it only acquires data when a new object is thrown into the container. When the container is opened and closed again, the system activates itself, runs the computation, stores the results and deactivates itself. The opening is detected by a metallic switch. The saved data is transferred wirelessly to the CC. In addition to these methods, when the container is full, the system ceases acquiring data and sends a warning message. It waits until the container is emptied and then it is reactivated. 5. Operating prototype and testing After the TP testing, the OP is undertaken (Fig. 3) with the help of the industrial partner D’Appolonia (DAPP). The elaboration unit (EU/PC104) is configured with Windows XP Embedded OS and the Advantech drivers are installed. The external interfaces let the user access directly the embedded OS via the standard interfaces (keyboard, mouse and CRT monitor).
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The device is equipped with general-purpose multiple sources for collecting a large amount of data during the evaluation phase. The development of such a knowledge base is finalized and relevant information will be used for the final system. The container is tested under actual conditions in Pudong; therefore the equipment has been made completely autonomous and independent from external energy sources or elaboration units.
Fig. 3. Two operating prototypes in the SJTU Lab in Shanghai.
The OP is developed in order to provide the same application features as the TP by the implementation of the complete procedure: data sampling, processing, data and error logging and data transmission. The difference from the TP is the fact that the OP can be tested in the field, as well as the presence of a GPRS module for data transmission. The code for application is developed using the C++ language, compiled in MS VC++ 6, using the I/O module libraries for signal connections and windows standard libraries for file system handling. In July 2007 two container prototypes (OPs) were prepared and made available at the SJTU Lab in Shanghai for testing and calibration before setting up the trials phase. After this initial testing and debugging period, the actual field tests are organized and undertaken. The field tests took place from August to October 2007 in Pudong, New Area. As a key source of information inside the system, the intelligent container was developed in order to provide wide coverage of content sampling and analysis. The prototypes are equipped with full sensors and monitoring devices assembled into a standard 240-L container, commonly used in Pudong residential areas. The main task of the intelligent containers inside the information flow is, therefore, to provide a summary record of content every time each container is opened for filling. In this way: A statistics knowledge base can be populated about the specific target area waste data. The wide range of sensors used to collect data on the container provides a coverage for formerly only partially known information.
All these general features for the container development are intended to focus on the flexibility of the solution, in order to supply a fully equipped, fully connected and customizable device for any kind of application and test. In particular, the prototype implementation – suitable for open environment use – is a key feature for the integration of information inside the Pudong context. In order to achieve such environmental condition objectives, the container devices underwent an extended test in Pudong from July to October 2007 (Fig. 4). The ‘‘trials” phase was successfully conducted by the contribution of all partners (POLIMI for design of the containers and the tests, DAPP for the hardware integration, SJTU for local support on data retrieval and communication, and PTPE for execution of the tests). For the purpose of system validation, some collection typologies are discussed in order to ad hoc configure the system: Residential collection: standard conditions of waste collection. The containers are placed close to the dumping area in residential compounds, waiting for the normal flow of waste from residents. The operators of PTPE/Puhuan assist and supervise the use of the container, visually checking the data transmission operation, autonomously done by the containers. The locations of this type are selected for testing the kind of waste that is commonly produced in such areas, providing a wide range of waste typology. In this way the optimization system collects different kinds of source data. The container prototype is used as a normal container and the data is transmitted wirelessly to the control centre. Mall collection: special conditions of collection. The container content in these locations varies significantly over time. Different kinds of waste and different volumes of waste are expected during the various periods of a day. Small compaction station: availability of a large amount of waste for populating a large database. Waste from different places is collected here for compaction. Before this procedure, the waste is thrown into the intelligent container for data collection. In this way, a large amount of data can be quickly acquired. In order to address all the garbage collection profiles, four main locations were selected for testing (Table 1).
Fig. 4. Photos from the actual testing of the operating prototypes in Pudong New Area, Shanghai.
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Table 1 Four test locations in Shanghai. Location
Location ID
Related test type
1957, Dongfang Road 180, Linyi Road 2140, South Yanggao Road 668, Chengshan Road
1 2 3 4
1 2 1 3
6. OP functionality and post-testing considerations Under the assumptions and the rationales about the development of the weighing system, the data collected during the trial phase are analyzed in order to address the information processing issues. In particular, the weight measurement is given by four different analog signals coming from each of the four load cells (cantilever metal beams with strain gauges). The raw signal is affected by the temperature drift and the offset reading because the sensors are sensitive to thermal effects and the weight measured is related to the single current value detected by the sensors. At every step of the signal processing, the offset must, therefore, be computed (Doebelin, 2004) and removed considering either the initial condition (offset value at empty container) or the partially full condition (previous content not to be counted for the current weight value). The initial offset value is detected and removed whenever the container is identified as ‘‘empty” by means of the US ranger sensor. The weight sensor signal is in this case recorded into a log-file for further removal during the following steps. The common condition of progressive container filling is instead processed, removing the previous step value of weight from the current one and updating the offset value. In this way every step is recorded with the current incremental value of the weight. As regards the thermal effect, the calibration procedure provides both the information about the sensor calibration curve (signal-weight value transform) and the thermal drift with fixed load at different temperatures. The weight final value is provided at reference temperature (36.9 °C). Hence, the raw analog signal is corrected, removing the temperature drift effect and adding a calibrated rate of deviation of the electrical response of the sensors proportional to the temperature reading. Then the analog signal is ready to be transformed into the weight value through the transformation constant. Finally, due to the integration and design issues, the four sensors provide different values on the basis of the centre of load on the OP bottom. Unbalanced loads (Kuehle-Weidemeier et al., 2007) produce deviations in the four signals; hence they are averaged whenever the deviation among the signals is not too significant. Otherwise, the four signals are compensated through four corresponding coefficients that take into account the distribution of loads. The larger the distance of the load centre from the geometrical centre of the bottom, the more the weights measured at larger distance are reduced in contribution to the overall average. In this way, the overload effect on a single sensor is reduced and the underload effect on an unloaded sensor is compensated. Sometimes, especially in almost-empty container conditions, the signals happen to be quite different. This situation is progressively reduced with the filling of the OP when the load happens to exert a more uniform pressure on the base. The data recorded and sent by the OPs contain also the separate raw analog signal values for each one of the weighing sensors. This is done in order to have an opportunity to post-process the data in order to better estimate the weight value during the trial phase. At each time-step of opening/closing the container, the level of filling is sampled (Fig. 5) and related to both the percentage of content level and the distance to be used as a reference for any pixel-distance conversion in image processing applications. The
Fig. 5. Level of filling of OP1 and weight of the waste, as calculated by the operating prototype.
volume is, therefore, computed using the information about the filling level and the features extracted by the image processing. The software used for the image processing is based on the motion detection methodology. The idea is to compare the last picture of the container’s content with the previous one in order to examine only the newest garbage thrown into it. For this to be achieved the photos have first to be converted from RGB into grayscale images. After that, the two images are subtracted in order to obtain the image of the differences between the two images. The image of the differences has then to be converted into a binary black/white image for further processing. The threshold for this procedure is calculated using the Otsu methodology. The image processing permits the identification of new objects thrown in the container. The new object is seen as a blob, and several characteristics can be then computed or estimated. There can be more than one new object thrown in the container simultaneously. For each and every object recognized, the properties of area, centre of mass, equivalent diameter, eccentricity, and minor and major axes length are calculated. All these calculations are made in pixel units. Using this information, an estimation of the object’s circularity can be made and this information is also saved. This is very important for the calculation of the object’s volume. The processed image is saved on the system’s hard disk. Every object’s area information is used in combination with the ultrasonic distance sensor measurement. In accordance with the object’s distance from the sensor and the sensor’s previous measurement, the object’s height is calculated. This permits the calculation of the object’s volume, when coupled with the area information. This cannot be done directly, since the area information is in pixels. For the pixel to centimeter conversion, experimental measurements have been made in order to obtain the different conversion ratios for the different object heights. After the conversion and according to the object’s circularity, the volume is computed and saved. The information on the volume and the weight can determine for every single object a mean density value, very useful for estimating the objects’ material. All the other information collected by the sensors (temperature, humidity and liquid height) is saved among the rest of the data and transmitted to the control centre. 7. Summary and conclusions The CleanWings project started by setting some objectives and expecting some outcomes. The expected outcomes have been
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reached and also success has been seen in other areas. The project studied and developed an innovative software for handling waste in a large city. In the waste collection container, several parameters are measured and with the help of the software these data are analyzed and compared. The outcome of this procedure is data on the actual weight and volume of the waste present in every container of the city. The information is updated every time a container is opened and closed back again. The information is sent to a control centre, which takes all the information about waste treatment. The project, SJTU side, also prepared the software for the control centre, which receives, via GPRS, all the information from the containers and creates a virtual map of the city with the position of every container and its level of fill. This information permits the optimization of the waste collection trucks’ trajectory. In this way money and time can be saved. These procedures are presented in a separate article. The project studied, chose and integrated the necessary hardware for the procedures earlier mentioned. The choice of this hardware was one of the most difficult tasks in the project, keeping in mind that the system should work under very difficult conditions and also that the cost should remain at a reasonable level. Nevertheless, the project also achieved this goal, providing as an output an innovative container for monitoring waste type and volume. The hardware chosen by the project has been carefully tested during the pre-prototype phase and it is still being tested in Pudong in the final prototypes, for possible future developments. All the above outcomes have resulted to a global waste control system, capable of monitoring an entire city or area of a city. CleanWings has achieved this goal by integrating all the different parts into a unique managing control centre. The tests that have been conducted have produced some interesting results, which are available on the CleanWings ftp site. The more homogeneous the waste contained in the container, the better it can be treated and less energy is needed for the procedure. In this way, less energy used leads to less heating and better environmental conditions. The impact of this can be a better quality of life for every citizen. In addition to that, the better waste sorting (SINTEF, 2007) achieved by using the system here proposed and the homogeneity of the materials produced after the introduction of the system should lead to a reduction in amount of waste disposed, which is one of the most serious problems. That is because the waste treatment facility should be able to work with much higher levels of efficiency, once the system here proposed is used. Also, the fact that the presence of water will be recognized by the system can drastically solve the corrosion problem, also present today in most waste treatment facilities. The CleanWings system could contribute to improve the homogeneity of materials inside waste incineration plants and waste disposal facilities. Homogeneity in materials is a key factor for waste treatment in determining the final output of the process and the related environmental impact. Separate waste collection is not enough for correct waste sorting (Schraft et al., 1996). People do not always put their garbage in the proper waste collection container. In addition to that, no information is available about the waste contained in the undifferentiated waste container. With the help of the system here proposed, a much better level of waste sorting can be achieved. For every container, the contents of the
waste container can be evaluated in terms of material homogeneity, weight, liquid contained, etc. The system will help distinguish the containers with homogenous waste from the ones that contain unwanted materials. In this way, all homogenous waste could be directly driven to the incineration plant, while the rest of the waste could be further evaluated before being transported to its final destination, resulting in an increase in the plant’s efficiency in terms of manpower use and energy consumption. The tests in the Pudong New Area with the operating prototypes helped verify the applicability of the system here discussed. Using the test results and the OP experience, an industrial prototype of this system is being developed, which will soon be tested in Shanghai. Acknowledgements This work was supported by the Sino-Italian Cooperation Program (CleanWings: Intelligent Supervision for Big Area Waste Disposal System, Project Number: C/II/S/07/025). FECO/SEPA and Sino-Italian Cooperation Program Shanghai Office are gratefully acknowledged. We also would like to express our acknowledgement to Pudong Environmental Protection Bureau and especially Mr. Zhang Pei Jun for guidelines and commitments and Pudong Solid Waste Administration Office for providing necessary detailed data and the help of investigation for this work. The authors want also to thank the Italian Ministry for Environment, Land and Sea for funding and management support, D’Appolonia SpA for system integration support and Pucheng TPE Ltd. for support in tests. References Ahluwalia, P.K., Nema, A.K., 2006. Multi-objective reverse logistics model for integrated computer waste management. Waste Management and Research 24 (6), 514–527. Doebelin, E., 2004. Measurement Systems: Applications and Design. McGraw Hill. Klaassen, K.B., 1996. Electronic Measurement and Instrumentation. Cambridge University Press. Kuehle-Weidemeier, M., Hohmann, F., Graf, J., 2007. Sampling and conditioning of waste samples. In: International Symposium MBT. Lunkapis, G.J., 2004. GIS as decision support tool for landfills siting. In: Map Asia Conference, China. O’Dell, T.H., 1991. Circuits for Electronic Instrumentation. Cambridge University Press. Ogra, A., 2003. Logistic Management and Spatial Planning for Solid Waste Management Systems using Geographical Information System, Map Asia, India. Omuta, G.E.D., 1987. Camouflage, contravention or connivance: towards an examination of development control in Bendel State, Nigeria. Third World Planning Review 3 (1), 135–153. Pudong Solid Waste Administration Office (PSWAO), 2006. Pudong Solid Waste Management Annual Report. Rotich, K. Henry, Yongsheng, Zhao, Jun, Dong, 2006. Municipal solid waste management challenges in developing countries – Kenyan case study. Waste Management 26 (1), 92–100. Sangwine, S.J., 1994. Electronic Components and Technology. CRC Press. Schraft, R.D., Wolf, A., Erhardt, S., 1996. A Robot System for Automatic Waste Sorting. SINTEF, 2007. Internet Published Article. . Solano, E., Ranjitha, S.R., Barlaz, M.A., Brill, E.D., 2005. Life-cycle based solid waste management. I: model development. Journal of Environmental Engineering, October. The World Bank, 2005. Waste management in China: issues and recommendations. Working Paper No. 9. Urban Development Working Papers. East Asia Infrastructure Department, Washington, DC. Tinmaz, E., Demir, I., 2005. Research on Solid Waste Management Systems: To Improve Existing Situation in Corlu Town of Turkey, Science Direct.
