Chemical Study of Wood Chip Drying: Biodegradation of Organic Pollutants in Condensate Waters from the Drying Process

In 2008, the European Union Commission put forward a proposal for a new directive on renewable forms of energy. Each of the member states should increase its share of renewable energies in an effort to boost the total share of the EU from the current 8.5% to 20% by 2020. The level of renewable energy in Finland was 28.5% in 2005, and the national target share of renewable energy by 2020 is 38%. To assist in reaching this goal, one solution is to further develop drying techniques of biomasses and utilization of untapped biomasses, because in Finland, up to 20 million tons of waste wood biomass per year are left unused during forestry operations. In this study the drying of biomasses and the condensate, including biodegradation of organic pollutants, is examined. The cost-efficiency and energy-efficiency of wood chips combustion can be increased by drying the wood to optimize moisture content before being utilized in different applications. However, the removal of moisture is the most expensive unit process in combustion. Studied wood species were pine (Pinus sylvestris), spruce (Picea abies), aspen (Populus tremula) and two birch species (Betula pendula and Betula pubescens). The biodegradation degrees of studied organic pollutants in condensing waters were between 25% and 61%. Furthermore, the biodegradation of organic compounds in condensing waters showed that at least the studied condensates can be safely disposed via municipal wastewater treatment plant (WWTP)

Recognizing steel plate side edge shape automatically using classification and regression models

Abstract In the steel plate production process it is important to minimize the wastage piece produced when cutting a mother steel plate to the size ordered by a customer. In this study, we build classi?cation and regression models to recognize the steel plate side edge shape, if it is curved or not and the amount of curvature. This is done based on time series data collected at the manufacturing line. In addition, this information needs to be presented in a way that enables fast analysis and long-term statistical monitoring. It can then be used to tune the parameters of the manufacturing process so that optimal curvature can be found and the size of the wastage piece can be reduced. The results show that using the classi?cation and linear regression methods, the side edge shape can be recognized reliably and the amount of curvature can be estimated with high accuracy as well