Optimization of thermo-chemical hydrolysis of kitchen wastes

Municipal Solid Wastes (MSWs) in Greece consist mainly of fermentable organic material such as food scraps (～50%) and paper residuals (～20%). The aim of this work was to study the thermo-chemical pretreatment of the kitchen waste (KW) fraction of MSW focusing on biotechnological exploitation of pretreated wastes for biofuel production. A representative sample of municipal food residues was derived by combining weighted amounts of each individual type of residue recognized in daily samples obtained from the University of Patras’ students restaurant located at the Students Residence Hall (Greece). Chemical pretreatment experiments of the representative KW sample were performed using several types of chemical solutions (i.e. H₂SO₄, HCl, NaOH, H₂SO₃) of different solute concentration (0.7%, 1.5%, 3%) at three temperatures (50, 75, 120 °C) and a range of residence times (30–120 min). Optimized results proved that chemical pretreatment of KW, using either 1.12% HCl for 94 min or 1.17% HCl for 86 min (at 100 °C), increased soluble sugars concentration by 120% compared to untreated KW. The increase of soluble sugars was mainly attributed to the mono-sugars glucose and fructose.     

Environmental Information, Asymmetric Information, and Financial Markets: A Game-Theoretic Approach

This paper examines the problem of asymmetric information in financial markets due to a lack of essential environmental information. The literature indicates that asymmetric information generates various problems for the actors of financial markets such as incomplete information for investment decisions and lending procedures, misallocation of financial market funds, the underestimating of stock price securities, and poor environmental risk management choices. To this end, this paper develops a game-theoretic approach to examine both the persistent nature of asymmetric information caused by the absence of accurate environmental information and to indicate how a well-organized, trustworthy, internationally agreed auditing accounting certification scheme could play a critical role in limiting the magnitude of this problem.     

Neural Network Model for Predicting Peak Photochemical Pollutant Levels

ABSTRACT

In this paper, an attempt is made for the 24-hr prediction of photochemical pollutant levels using a neural network model. For this purpose, a model is developed that relates peak pollutant concentrations to meteorological and emission variables and indexes. The analysis is based on measurements of O₃ and NO₂ from the city of Athens. The meteorological variables are selected to cover atmospheric processes that determine the fate of the airborne pollutants while special care is taken to ensure the availability of the required input data from routine observations or forecasts. The comparison between model predictions and actual observations shows a good agreement. In addition, a series of sensitivity tests is performed in order to evaluate the sensitivity of the model to the uncertainty in meteorological variables. Model forecasts are generally rather insensitive to small perturbations in most of the input meteorological data, while they are relatively more sensitive in changes in wind speed and direction.