Bioremediation of waste water to remove heavy metals using the spent mushroom substrate of Agaricus bisporus

The presence of heavy metals in waste water brings serious environmental pollution that threatens human health and the ecosystem. Bioremediation of heavy metals has received considerable and growing interest over the years. Thus, this paper presents the use of the Spent Mushroom Substrate (SMS) of Agaricus bisporus cultivation as a bioremediating agent to remove heavy metals that are present in industrial waters. These metals include chromium, lead, iron, cobalt, nickel, manganese, zinc, copper and aluminium. In particular, this study analyses the performance of SMS bioreactors with different groups of heavy metals at various concentrations. Between 80% and 98% of all contaminants that were analysed can be removed with 5 kg of SMS at hydraulic retention times of 10 and 100 days. The best removal efficiencies and longevities were achieved when removing iron (III), nickel and cobalt from contaminated water at a pH of 2.5. These results suggest that SMS can successfully treat waste water that has been contaminated with heavy metals

Aprendizaje del manejo de un sistema de cuchillas de soplado de una línea de galvanizado mediante simulación y realidad virtual

Congreso Internacional de Comunicación, Tecnología y Educación (3º. 2000. Oviedo

Optimizing biodiesel production fromwaste cooking oil using genetic algorithm-based support vector machines

The ever increasing fuel demands and the limitations of oil reserves have motivated research of renewable and sustainable energy resources to replace, even partially, fossil fuels, which are having a serious environmental impact on global warming and climate change, excessive greenhouse emissions and deforestation. For this reason, an alternative, renewable and biodegradable combustible like biodiesel is necessary. For this purpose, waste cooking oil is a potential replacement for vegetable oils in the production of biodiesel. Direct transesterification of vegetable oils was undertaken to synthesize the biodiesel. Several variables controlled the process. The alkaline catalyst that is used, typically sodium hydroxide (NaOH) or potassium hydroxide (KOH), increases the solubility and speeds up the reaction. Therefore, the methodology that this study suggests for improving the biodiesel production is based on computing techniques for prediction and optimization of these process dimensions. The method builds and selects a group of regression models that predict several properties of biodiesel samples (viscosity turbidity, density, high heating value and yield) based on various attributes of the transesterification process (dosage of catalyst, molar ratio, mixing speed, mixing time, temperature, humidity and impurities). In order to develop it, a Box-Behnken type of Design of Experiment (DoE) was designed that considered the variables that were previously mentioned. Then, using this DoE, biodiesel production features were decided by conducting lab experiments to complete a dataset with real production properties. Subsequently, using this dataset, a group of regression models—linear regression and support vector machines (using linear kernel, polynomial kernel and radial basic function kernel)—were constructed to predict the studied properties of biodiesel and to obtain a better understanding of the process. Finally, several biodiesel optimization scenarios were reached through the application of genetic algorithms to the regression models obtained with greater precision. In this way, it was possible to identify the best combinations of variables, both independent and dependent. These scenarios were based mainly on a desire to improve the biodiesel yield by obtaining a higher heating value, while decreasing the viscosity, density and turbidity. These conditions were achieved when the dosage of catalyst was approximately 1 wt %

Técnicas y Algoritmos Básicos de Visión Artificial

El libro surge de las experiencias obtenidas, en estos últimos años, de varios Proyectos de Visión Artificial que han realizado los autores en varias empresas del sector Agroalimentario e Industrial. Éste libro, pretende ser una pequeña recopilación de aquellas técnicas y algoritmos que consideramos han resultado más efectivos, fundamentalmente por su sencillez y eficacia