The aim of the study is to evaluate the performance of various mathematical modelling methods, while forecasting medical waste generation using Lithuania's annual medical waste data. Only recently has a hazardous waste collection system that includes medical waste been created and therefore the study access to gain large sets of relevant data for its research has been somewhat limited. According to data that was managed to be obtained, it was decided to develop three short and extra short datasets with 20, 10 and 6 observations. Spearman's correlation calculation showed that the influence of independent variables, such as visits at hospitals and other medical institutions, number of children in the region, number of beds in hospital and other medical institutions, average life expectancy and doctor's visits in that region are the most consistent and common in all three datasets. Tests on the performance of artificial neural networks, multiple linear regression, partial least squares, support vector machines and four non-parametric regression methods were conducted on the collected datasets. The best and most promising results were demonstrated by generalised additive (R(2) = 0.90455) in the regional data case, smoothing splines models (R(2) = 0.98584) in the long annual data case and multilayer feedforward artificial neural networks in the short annual data case (R(2) = 0.61103).
New possible ways of plastics manufacture and waste treatment are being searched when trying to tackle the problems related to the growth of waste quantities and decline in non-renewable resources. Presently, the most promising and effective way to solve the mentioned problems is production of bioplastics, but its way to recognition is very slow. One of the barriers is the absence of clear and united opinion throughout the EU Arising new discussions about biodegradable and biobased plastics will allow responsible authorities to update and adapt the legal law, which now almost does not include any reglement on bioplastics production and usage. The other issues of bioplastics are social aspects as this material can be made of primal food sources like crops, and the ecological ones because of their unknown effects on human health and the environment. Nevertheless, the main problem remains the same -the price of petro-plastics is still lower than that of bioplastics. Despite this, the biggest companies of the world are starting an initiative to manufacture this new kind of plastics and to widen the range of bioplastics usage in packaging. Considering today's situation and tendencies, at the end of this paper the recommendations for the improvement and speeding up of the processes related to bioplastics manufacture, usage and its waste management in Europe and Lithuania are presented.
The aim of the study was to create a hybrid forecasting method that could produce higher accuracy forecasts than previously used 'pure' time series methods. Mentioned methods were already tested with total automotive waste, hazardous automotive waste, and total medical waste generation, but demonstrated at least a 6% error rate in different cases and efforts were made to decrease it even more. Newly developed hybrid models used a random start generation method to incorporate different time-series advantages and it helped to increase the accuracy of forecasts by 3%-4% in hazardous automotive waste and total medical waste generation cases; the new model did not increase the accuracy of total automotive waste generation forecasts. Developed models' abilities to forecast short- and mid-term forecasts were tested using prediction horizon.
and development of effective hazardous waste utilization methods or the establishment of certain facilities.The overview of previously conducted research on waste matter in general showed that various mathematical forecasting methods were used to predict solid waste generation. Only a few published research papers that were able to be found using a wide access to scientific journals and subscribed scientific databases uncovered successful application of mathematical prognostic methods applicable for few types of hazardous waste, but not hazardous waste in general. This paper will only overview those research papers that showed the most promising forecasting results.A study conducted by Abdol et al. (2011) presented the approach to unravel the interpolating problem of various structures of artificial neural networks (ANN) for the long-term prediction of solid waste generation (SWG). Results indicate that the multilayer perception approach has more advantages in comparison with traditional methods, like MLR, in predicting the municipal SWG (Abdol et al. 2011).Artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the total rate of medical waste generation and in different types of sharp, infectious and general waste. ANNs showed high performance measure values (R 2 = 0,99 Abstract. Due to inefficient waste sorting in primary and secondary waste generation sources Lithuania fails in trying to meet EU requirements for waste management sector regarding the amount of waste flow that reaches landfills. Especially sensitive situation is with hazardous waste, which often are disposed along with municipal solid waste and with it reaches landfills and due to the fact that mechanical and biological treatment plant are only now being established in the biggest cities of Lithuania, landfills becomes a big issue. The main purpose of this research is to find out which mathematical modelling methods could be fitted and if it is possible to forecast annual hazardous waste generation by using automotive, medical and daylight lamps waste generation statistical data. This is part of a research of medical, automotive and daylight lamps waste generation forecasting possibilities. Tests on the performance of artificial neural networks, multiple linear regression, partial least squares, support vector machines and four nonparametric regression methods were conducted on two developed data sets. The best and most promising results in both cases were demonstrated by generalized additives method (R 2 = 0.99) and kernel regression (R 2 = 0.99).
The paper presents an analysis of problems which had to be dealt with by Lithuanian institutions while implementing environmental requirements laid down in Article 11 of European Parliament and Council Directive 94/62/EC on Packaging and Packaging Waste as well as in Decision 2009/292/EC of the European Commission establishing the conditions for derogation of plastic crates and plastic pallets related to heavy metal concentration limits set by Directive 94/62/EC. While the Directive puts ban on the usage of packaging with the aggregate concentration of 4 heavy metals (lead, cadmium, mercury and hexavalent chromium) exceeding 100 ppm, Decision 2009/292/EC, instead, allows their usage if terms of derogation specified in the Decision are met. The implementation of the Decision means that each crate and/or pallet item circulating in the market with the concentration of the 4 heavy metals above the set level has to be identified, accounted, traced while in service, must at the end of service be delivered into a controlled recycling system and finally recycled in a way specified by the Decision. Therefore, the establishment of such a country-wide system presents a challenging task for the country as EU legislation sets no common requirements for its structure and leaves it to the country's discretion. This paper systematises and summarises some principles and practices of managing the usage of plastic crates and plastic pallets containing the amount of heavy metals higher than 100 ppm (as set by Directive 94/62/EC) in the EU Member States. The paper analyses possibilities and offers several scenarios for implementation of Directive 94/62/EC with respect to plastic crates and pallets with high concentration of heavy metals in Lithuania. Both the Directive and the Decision are based on using the data available from bookkeeping conducted by owners of crates and pallets and the EU environmental accounting/control system used in the country. The offered mechanisms are analysed and compared between themselves as well as with analogue systems used in other EU countries. Besides, the problem of unidentified plastic crates and plastic pallets already in use is being discussed. A special decision-making tree was developed to allow splitting the stream of these items into 2 flows with the concentration of heavy metals below and above the 100 ppm limit.
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