Sizing of reactors by charts of Damköhler's number for solutions of dimensionless design equations.

The reaction kinetic rate and mass transport play an important role in the sizing and scale-up of reactors. The Damköhler's dimensionless number ( D a ) is the quotient of these effects. A new interpretation of D a as a local property is introduced D a ( x , y , z , t ) . A new graphical methodology is proposed for the sizing and scale-up of unidirectional flow reactors and CSTRs. The partial differential equation (PDE) and algebraic that describe the continuity within these reactors transform into dimensionless variables, and the conversion at the output is expressed as a function of the conditions at the input D a 0 . The operating conditions as volumetric flow, residence time; design variables as reactor volume; and intrinsic reaction rate are involved in D a 0 . The equations are solved numerically to develop the design charts D a 0 vs X. The design volume is linear with D a 0 , and the conversion is obtained from the charts ( D a 0 vs X) or vice versa. Using these charts avoids the analytical or numerical solution of the PDE that governs the unidirectional flow reactors becoming an easy tool for scale-up. The article portrays how to use these diagrams. Reactors with D a 0 < 0.1 have a low conversion per pass, the charts also allow estimating the number of recirculations required as a function of the overall conversion. Reactors with the same conversion have the same D a 0 , both laboratory and industrial scale. Then, the D a number is presented as a fundamental parameter for design and scaling-up these reactors.

Energy Return on Investment (EROI) and Life Cycle Analysis (LCA) of biofuels in Ecuador.

In Ecuador, the net energy contribution of biofuels is unknown or unnoticed. To address this issue, we determined the Energy Return on Investment (EROI) for bioethanol and biodiesel. The selection of raw materials relied on their productive capacity, export and import records, and historical yields. Consequently, the scope included three raw materials for ethanol (sugar cane, corn, and forest residues) and four for biodiesel (African palm, pinion, bovine fat, and swine fat). Using a method based on the Life Cycle Analysis (LCA) of each biofuel, we assessed the entire production chain through statistical processing of primary and secondary information. Then we calculated the calorific values in the laboratory, compared energy inputs/outputs, and finally obtained the energetic returns. EROIs for bioethanol were: 1.797 for sugarcane, 1.040 for corn, and 0.739 for wood. The results for biodiesel were: 3.052 for African palm, 2.743 for pinion, 2.187 for bovine fat, and 2.891 for swine fat. These values suggest feasibility only for sugarcane in the case of ethanol. In contrast, biodiesel has better prospects because all the feedstocks analyzed had EROIs higher than two. Nevertheless, biodiesel is not available for trading in Ecuador because energy policy has overlooked systems based on higher energy return. Future studies should consider more comprehensive variables such as climate change, land use, and water management.

Evaluation of low-cost alternatives for water purification in the stilt house villages of Santa Marta's Ciénaga Grande.

Water purification is indispensable to guarantee safe human consumption and to prevent diseases caused by the ingestion of contaminated water. This requires a series of water treatment processes which require investment. However, the economic limitations of rural communities hinder their ability to implement such water-treatment systems, as is the case in Ciénaga Grande of Santa Marta ("Large Swamp", in English) in Colombia. Low-cost systems can be used instead as simple and safe alternatives. Therefore, the objective of this work was to evaluate non-conventional, low-cost water processes to purify the water from the collection point of two stilt house villages in Ciénaga Grande of Santa Marta. These include: 1) Using two natural coagulants, Moringa Oleifera and Cassia Fistula; 2) filtration through a biosand filter and a carbon activated filter; and 3) disinfection through UV-C Radiation and through solar disinfection. The results showed a turbidity values reduction between 52% and 96% using the two natural coagulants; both turbidity and total coliforms achieved reductions of 98.4% and 76.9%, respectively in the filtration process; and removal of total coliforms up to 98.8% in the disinfection process. Despite the high rates of reduction in the different parameters, the water does not comply with the recommended limits for safe drinking water.

Predictive analysis of urban waste generation for the city of Bogotá, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks.

This study presents an analysis of three models associated with artificial intelligence as tools to forecast the generation of urban solid waste in the city of Bogotá, in order to learn about this type of waste's behavior. The analysis was carried out in such a manner that different efficient alternatives are presented. In this paper, a possible decision-making strategy was explored and implemented to plan and design technologies for the stages of collection, transport and final disposal of waste in cities, while taking into account their particular characteristics. The first model used to analyze data was the decision tree which employed machine learning as a non-parametric algorithm that models data separation limitations based on the learning decision rules on the input characteristics of the model. Support vector machines were the second method implemented as a forecasting model. The primary advantage of support vector machines is their proper adjustment to data despite its variable nature or when faced with problems with a small amount of training data. Lastly, recurrent neural network models to forecast data were implemented, which yielded positive results. Their architectural design is useful in exploring temporal correlations among the same. Distribution by collection zone in the city, socio-economic stratification, population, and quantity of solid waste generated in a determined period of time were factors considered in the analysis of this forecast. The results found that support vector machines are the most appropriate model for this type of analysis.

Development of high quality activated carbon from biological sludge and its application for dyes removal from aqueous solutions.

A high quality activated carbon was developed from biological sludge of a beverage wastewater treatment plant (BWTP). The material was characterized and its adsorption potential to remove Allura Red AC and Crystal Violet dyes from aqueous media was verified. The ACBS (activated carbon from beverage sludge) revealed mesoporous features, presenting average pore diameter of 6.32 nm, pore volume of 0.5098 cm3 g-1 and surface area of 631.8 m2 g-1. Adsorption was adequate using 0.25 g L -1 of ACBS, and, the process was favored at pH 2.0 for Allura Red AC and pH 8.0 for Crystal Violet. From the kinetic viewpoint, the data were satisfactorily represented by the pseudo-second order model. Freundlich and Sips models were suitable to represent the adsorption equilibrium of the Allura Red and Crystal Violet, respectively. The maximum values for adsorption capacities were 287.1 mg g-1 for Allura Red and 640.7 mg g-1 for Crystal Violet. The adsorption of both dyes was thermodynamically spontaneous, favorable and endothermic. In brief, the residual sludge of a wastewater treatment plant may be used as an eco-friendly precursor for ACBS production. ACBS was an efficient adsorbent material able to uptake dyes from aqueous solutions.