Accelerating preliminary eco-innovation design for products that integrates case-based reasoning and TRIZ method

While the innovativeness and market potential of a new product is normally emphasized, its environmental impact is often neglected. Despite their ability to help designers reduce the environmental impact of products throughout their life cycle, many eco-design methods focus on the redesign of existing products, necessitating a more innovative design capacity. This work describes a novel model to accelerate the preliminary eco-innovation product design by integrating the advantages of case-based reasoning and the TRIZ method. Previous cases from a database support a novel design to satisfy functional performances under the CBR framework. The innovative principles and evolution patterns of the TRIZ method can enhance the design level of new products to achieve eco-innovation. Four eco-innovation concepts for a PC mouse demonstrate the effectiveness of the proposed model.     

Oil spreading in instantaneous and continuous spills on rotating earth

The effect of the Coriolis force on the oil spill spreading in the gravity-viscous regime is examined. A new shallow water model for the transport and spreading of oil slick of arbitrary shape is described in which the Coriolis force is included in the momentum equations and the oil–water friction is parameterized in a frame of the boundary layer theory including the Ekman friction. The numerical Lagrangian method based on smoothed particle dynamics is described. New similarity solutions of the model equations are obtained for unidirectional and axisymmetric spreading in gravity-viscous, gravity-turbulent and gravity-viscous-rotational regimes for instantaneous as well as continuous releases. The numerical simulation extends these results for the case of continuous release in the presence of currents. It was shown that Coriolis term in the momentum equation can be omitted if slick thickness is much less of the laminar Ekman layer thickness. However, the Ekman friction should be retained for slicks of any thickness for larger times. The Ekman friction results in the essential slowdown of the spreading as well as in the deflection of the oil spreading velocity at 45° from the direction of velocity in the non-rotation case. Numerical simulations of large-scale spills showed that after the 2?days the slick area with the Coriolis effect was approximately less than half of that without rotation. Therefore, the earth rotation can be also important in the oil weathering.     

Toxicity of atrazine and its bioaccumulation and biodegradation in a green microalga,Chlamydomonas mexicana

This study evaluated the toxicity of herbicide atrazine, along with its bioaccumulation and biodegradation in the green microalga Chlamydomonas mexicana. At low concentration (10 μg L^?1), atrazine had no profound effect on the microalga, while higher concentrations (25, 50, and 100 μg L^?1) imposed toxicity, leading to inhibition of cell growth and chlorophyll a accumulation by 22 %, 33 %, and 36 %, and 13 %, 24 %, and 27 %, respectively. Atrazine 96-h EC50 for C. mexicana was estimated to be 33 μg L^?1. Microalga showed a capability to accumulate atrazine in the cell and to biodegrade the cell-accumulated atrazine resulting in 14–36 % atrazine degradation at 10–100 μg L^?1. Increasing atrazine concentration decreased the total fatty acids (from 102 to 75 mg g^?1) and increased the unsaturated fatty acid content in the microalga. Carbohydrate content increased gradually with the increase in atrazine concentration up to 15 %. This study shows that C. mexicana has the capability to degrade atrazine and can be employed for the remediation of atrazine-contaminated streams.     

Metal distribution in incineration residues of municipal solid waste (MSW) in Japan

This study aimed to identify distribution of metals and the influential factors on metal concentrations in incineration residues. Bottom ash and fly ash were sampled from 19 stoker and seven fluidized bed incinerators, which were selected to have a variety of furnace capacity, furnace temperature, and input waste. In the results, shredded bulky waste in input waste increased the concentration of some metals, such as Cd and Pb, and the effect was confirmed by analysis of shredded bulky waste. During MSW incineration, lithophilic metals such as Fe, Cu, Cr, and Al remained mainly in the bottom ash while Cd volatilized from the furnace and condensed to the fly ash. About two thirds of Pb and Zn was found in the bottom ash despite their high volatility. Finally, based on the results obtained in this study, the amount of metal in incineration residues of MSW was calculated and the loss of metal was estimated in terms of mass and money. A considerable amount of metal was found to be lost as waste material by landfilling of incineration residues.     

EPR characterization of the catalytic activity of clays for PCE removal by gamma-radiation induced by acid and thermal treatments

Clays from tidal flat sediments showed efficient catalytic activity in the decomposition of PCE by gamma-radiation. The highest PCE removal of 98.6% was obtained with clays heated to 700 degrees C after acid treatment. The improved catalytic activity was identified by electron paramagnetic resonance (EPR) spectroscopy. The EPR spectra of clays were significantly changed by the acid and thermal treatments. The intensity of a narrow signal at g=2 (signal III) was decreased with increasing thermal treatment temperature and this increased the PCE removal efficiency. The acid treatment completely removed a broad signal at g=2 (signal II), decreased the intensity of signal III, and improved the gamma-radiation treatment of PCE.     

Selective leaching of valuable metals from waste printed circuit boards

This study was carried out to recover valuable metals from the printed circuit boards (PCBs) of waste computers. PCB samples were crushed to smaller than 1 mm by a shredder and initially separated into 30% conducting and 70% nonconducting materials by an electrostatic separator. The conducting materials, which contained the valuable metals, were then used as the feed material for magnetic separation, where it was found that 42% of the conducting materials were magnetic and 58% were nonmagnetic. Leaching of the nonmagnetic component using 2 M H2SO4 and 0.2 M H2O2 at 85 degrees C for 12 hr resulted in greater than 95% extraction of Cu, Fe, Zn, Ni, and Al. Au and Ag were extracted at 40 degrees C with a leaching solution of 0.2 M (NH4)2S2O3, 0.02 M CuSO4, and 0.4 M NH4OH, which resulted in recovery of more than 95% of the Au within 48 hr and 100% of the Ag within 24 hr. The residues were next reacted with a 2 M NaCl solution to leach out Pb, which took place within 2 hr at room temperature.     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen(T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks(ANNs) and support vector machines(SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination(R~2), Nash–Sutcliff efficiency(NSE), relative efficiency criteria(d rel). Additionally, Latin-Hypercube one-factor-at-a-time(LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.