Gasification characteristics of biomass for tar removal by secondary oxidant injection

Gasification experiments for sawdust were conducted using a fixed bed reactor at 900 °C by varying the secondary oxidant injection ratio to determine the optimal conditions for tar removal along with the enhancement of gasification efficiency. Secondary oxidant was injected as an oxidant at the top zone of the gasifier in varying ratios of 10–30% of the total amount of oxidant. This method was based on the primary method of tar removal and gasification efficiency improvement by thermal cracking of tar. Various gasification performance parameters were evaluated and tar content was estimated by measuring the fluctuation of weight of the activated carbon filter. The results showed that the concentration of tar in the producer gas decreased by injecting the secondary oxidant, even though syngas yield decreased. The recycling potential of the char produced in the gasification experiments was also assessed with the purpose of utilizing char as an adsorbent by determining its surface area and pore volume. The results demonstrated that the char produced from the gasification experiment had similar quality to that of the activated carbon used in this experiment.     

Absorption characteristics of potassium carbonate-based solutions with rate promoters and corrosion inhibitors

In this research, absorbents for CO₂ capture were prepared by blending 30 wt% potassium carbonate, 3 wt% of a rate promoter, and 1 wt% of a corrosion inhibitor. Pipecolic acid, sarcosine, and diethanolamine were chosen as rate promoter candidates. Based on a rate promoter screening test for CO₂ loading capacity and absorption rate, pipecolic acid and sarcosine were selected to be used as rate promoters. 1,2,3-benzotriazole and ammonium thiocyanate were chosen as corrosion inhibitors, and they were mixed with a 30 wt% potassium carbonate-based absorbent mixture containing one of the rate promoters. The absorption rates for four absorbent solutions (30 wt% potassium carbonate?+?3 wt% pipecolic acid?+?1 wt% 1,2,3-benzotriazole, 30 wt% potassium carbonate?+?3 wt% pipecolic acid?+?1 wt% ammonium thiocyanate, 30 wt% potassium carbonate?+?3 wt% sarcosine?+?1 wt% 1,2,3-benzotriazole, and 30 wt% potassium carbonate?+?3 wt% sarcosine?+?1 wt% ammonium thiocyanate) were measured, tabulated, and graphically displayed. These types of absorbents can be used for capturing CO₂ under high temperature and pressure conditions, such as those found in coal-fired power plants.     

Catalytic upgrading pyrolysis of pine sawdust for bio-oil with metal oxides

The catalytic upgrading pyrolysis of pine sawdust was performed at 500 °C with various metal oxides to improve the quality of the bio-oil. The aim of this study was to investigate the potential of the metal oxides instead of traditional zeolites for catalytic upgrading pyrolysis with the analysis of Gas Chromatograph/Mass Spectrometer. In this study, the used catalysts were Calcium-oxide, Magnesium oxide, Titanium dioxide, and Zeolite (Si/Al?=?80). The influence of catalysts on products yields and compositions were investigated. Most metal oxides can enhance the bio-gas with the bio-oil yields decreased. The metal oxides led to a decrease of Acids, Aldehydes, Ketones and an increase of Furfural, Cresols, Catechols in Furans and Phenolics. Among the catalysts, the MgO catalysts was the most effective to convert the high molecular into lights ones (6.65% Cresols) with yield of 20.48% for Furfural. The deoxygenation reaction in bio-oil was suggested to convert oxygenated compounds into the low molecular weight of the materials (6.39% Guaiacols). Thus, the used metal oxides can improve the quality of bio-oil by decreasing undesirable compounds as well as increasing the desirable compounds with low oxygen contents via deoxygenation reaction.     

Social media and disasters: a functional framework for social media use in disaster planning,response, and research

A comprehensive review of online, official, and scientific literature was carried out in 2012–13 to develop a framework of disaster social media. This framework can be used to facilitate the creation of disaster social media tools, the formulation of disaster social media implementation processes, and the scientific study of disaster social media effects. Disaster social media users in the framework include communities, government, individuals, organisations, and media outlets. Fifteen distinct disaster social media uses were identified, ranging from preparing and receiving disaster preparedness information and warnings and signalling and detecting disasters prior to an event to (re)connecting community members following a disaster. The framework illustrates that a variety of entities may utilise and produce disaster social media content. Consequently, disaster social media use can be conceptualised as occurring at a number of levels, even within the same disaster. Suggestions are provided on how the proposed framework can inform future disaster social media development and research.     

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.