ABSTRACT
In recent years, the production and consumption of fossil jet fuel have increased as a consequence of a rise in the number of passengers and goods transported by air. Despite the low demand caused by the coronavirus 2019 pandemic, an increase in the services offered by the sector is expected again. In an economic context still dependent on scarce oil, this represents a problem. There is also a problem arising from the fuel's environmental impact throughout its life cycle. Given this, a promising solution is the use of biojet fuel as renewable aviation fuel. In a circular economy framework, the use of lignocellulosic biomass in the form of sugar-rich crop residues allows the production of alcohols necessary to obtain biojet fuel. The tools provided by process intensification also make it possible to design a sustainable process with low environmental impact and capable of achieving energy savings. The goal of this work was to design an intensified process to produce biojet fuel from Mexican lignocellulosic biomass, with alcohols as intermediates. The process was modeled following a sequence of pretreatment/hydrolysis/fermentation/purification for the biomass-ethanol process, and dehydration/oligomerization/hydrogenation/distillation for ethanol-biojet process under the concept of distributed configuration. To obtain a cleaner, greener, and cheaper process, the purification zone of ethanol was intensified by employing a vapor side stream distillation column and a dividing wall column. Once designed, the entire process was optimized by employing the stochastic method of differential evolution with a tabu list to minimize the total annual cost and with the Eco-indicator-99 to evaluate the sustainability of the process. The results show that savings of 5.56% and a reduction of 1.72% in Eco-indicator-99 were achieved with a vapor side stream column in comparison with conventional distillation. On the other hand, with a dividing wall column, savings of 5.02% and reductions of 2.92% in Eco-indicator-99 were achieved. This process is capable of meeting a demand greater than 266 million liters of biojet fuel per year. However, the calculated sale price indicates that this biojet fuel still does not compete with conventional jet fuel produced in Mexico. © 2022 Society of Chemical Industry and John Wiley & Sons, Ltd. © 2022 Society of Chemical Industry and John Wiley & Sons, Ltd.
ABSTRACT
In recent years, the production and consumption of fossil jet fuel have increased as a consequence of a rise in the number of passengers and goods transported by air. Despite the low demand caused by the coronavirus 2019 pandemic, an increase in the services offered by the sector is expected again. In an economic context still dependent on scarce oil, this represents a problem. There is also a problem arising from the fuel's environmental impact throughout its life cycle. Given this, a promising solution is the use of biojet fuel as renewable aviation fuel. In a circular economy framework, the use of lignocellulosic biomass in the form of sugar-rich crop residues allows the production of alcohols necessary to obtain biojet fuel. The tools provided by process intensification also make it possible to design a sustainable process with low environmental impact and capable of achieving energy savings. The goal of this work was to design an intensified process to produce biojet fuel from Mexican lignocellulosic biomass, with alcohols as intermediates. The process was modeled following a sequence of pretreatment/hydrolysis/fermentation/purification for the biomass-ethanol process, and dehydration/oligomerization/hydrogenation/distillation for ethanol-biojet process under the concept of distributed configuration. To obtain a cleaner, greener, and cheaper process, the purification zone of ethanol was intensified by employing a vapor side stream distillation column and a dividing wall column. Once designed, the entire process was optimized by employing the stochastic method of differential evolution with a tabu list to minimize the total annual cost and with the Eco-indicator-99 to evaluate the sustainability of the process. The results show that savings of 5.56% and a reduction of 1.72% in Eco-indicator-99 were achieved with a vapor side stream column in comparison with conventional distillation. On the other hand, with a dividing wall column, savings of 5.02% and reductions of 2.92% in Eco-indicator-99 were achieved. This process is capable of meeting a demand greater than 266 million liters of biojet fuel per year. However, the calculated sale price indicates that this biojet fuel still does not compete with conventional jet fuel produced in Mexico. © 2022 Society of Chemical Industry and John Wiley & Sons, Ltd. © 2022 Society of Chemical Industry and John Wiley & Sons, Ltd.
ABSTRACT
Over the past few years, investigators in Brazil have been uncovering numerous corruption and money laundering schemes at all levels of government and in the country's largest corporations. It is estimated that between 2% and 5% of the global GDP is lost annually because of such practices, not only directly impacting public services and private sector development but also strengthening organized crime. However, most law enforcement agencies do not have the capability to carry out systematic corruption risk assessment leveraging on the availability of data related to public procurement. The currently prevailing approach employed by Brazilian law enforcement agencies to detect companies involved in potential cases of fraud consists in receiving circumstantial evidence or complaints from whistleblowers. As a result, a large number of companies involved in fraud remain undetected and unprosecuted. The decision support system (DSS) described in this work addresses these existing limitations by providing a tool for systematic analysis of public procurement. It allows the law enforcement agencies to establish priorities concerning the companies to be investigated. This DSS incorporates data mining algorithms for quantifying dozens of corruption risk patterns for all public contractors inside a specific jurisdiction, leading to improvements in the quality of public spending and to the identification of more cases of fraud. These algorithms combine operations research tools such as graph theory, clusterization, and regression analysis with advanced data science methods to allow the identification of the main risk patterns, such as collusion between bidders, conflicts of interest (e.g., a politician who owns a company contracted by the same government body where he or she was elected), and companies owned by a potentially straw person used for disguising its real owner (e.g., beneficiaries of cash conditional transfer programs). The DSS has already led to a detailed analysis of large public procurement datasets, which add up to more than 50 billion dollars. Moreover, the DSS provided strategic inputs to investigations conducted by federal and state agencies. © 2020 The Authors. International Transactions in Operational Research © 2020 International Federation of Operational Research Societies