The chemical oxygen demand / total volatile acids ratio as an anaerobic treatability indicator for landfill leachates

In some operational circumstances a fast evaluation of landfill leachate anaerobic treatability is necessary, and neither Biochemical Methane Potential nor BOD/COD ratio are fast enough. Looking for a fast indicator, this work evaluated the anaerobic treatability of landfill leachate from São Carlos-SP (Brazil) in a pilot scale Anaerobic Sequence Batch Biofilm Reactor (AnSBBR). The experiment was conducted at ambient temperature in the landfill area. After the acclimation, at a second stage of operation, the AnSBBR presented efficiency above 70%, in terms of COD removal, utilizing landfill leachate without water dilution, with an inlet COD of about 11,000 mg.L-1, a TVA/COD ratio of approximately 0.6 and reaction time equal to 7 days. To evaluate the landfill leachate biodegradability variation over time, temporal profiles of concentration were performed in the AnSBBR. The landfill leachate anaerobic biodegradability was verified to have a direct and strong relationship to the TVA/COD ratio. For a TVA/CODTotal ratio lower than 0.20, the biodegradability was considered low, for ratios between 0.20 and 0.40 it was considered medium, and above 0.40 it was considered high.

Métodos de previsão auto-regressivo aplicado a uma série de volume de produção de caminhões

The national truck fleet has expanded strongly in recent decades. However, due to fluctuations in the demand that the market is exposed, it needed up making more effective strategic decisions of automakers. These decisions are made after an evaluation of guaranteed sales forecasts. This work aims to generate an annual forecast of truck production by Box and Jenkins methodology. They used annual data for referring forecast modeling from the year 1957 to 2014, which were obtained by the National Association of Motor Vehicle Manufacturers (Anfavea). The model used was Autoregressive Integrated Moving Average (ARIMA) and can choose the best model for the series under study, and the ARIMA (2,1,3) as representative for conducting truck production forecast

Modelagem auto-regressiva na previsão de demanda dos produtos de uma empresa do setor químico

Considering the high competitiveness in the industrial chemical sector, demand forecast is a relevant factor for decision-making. There is a need for tools capable of assisting in the analysis and definition of the forecast. In that sense, the objective is to generate the chemical industry forecast using an advanced forecasting model and thus verify the accuracy of the method. Because it is time series with seasonality, the model of seasonal autoregressive integrated moving average - SARIMA generated reliable forecasts and acceding to the problem analyzed, thus enabling, through validation with real data improvements in the management and decision making of supply chain