Sequential application of soil vapor extraction and bioremediation processes for the remediation of ethylbenzene-contaminated soils

Soil vapor extraction (SVE) is an efficient, well-known and widely applied soil remediation technology. However, under certain conditions it cannot achieve the defined cleanup goals, requiring further treatment, for example, through bioremediation (BR). The sequential application of these technologies is presented as a valid option but is not yet entirely studied. This work presents the study of the remediation of ethylbenzene (EB)-contaminated soils, with different soil water and natural organic matter (NOMC) contents, using sequential SVE and BR. The obtained results allow the conclusion that: (1) SVE was sufficient to reach the cleanup goals in 63% of the experiments (all the soils with NOMC below 4%), (2) higher NOMCs led to longer SVE remediation times, (3) BR showed to be a possible and cost-effective option when EB concentrations were lower than 335 mg kgsoil −1, and (4) concentrations of EB above 438 mg kgsoil −1 showed to be inhibitory for microbial activity.

Application-specific modified particle swarm optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding he management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding the management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Development of computer application for determination of chemical diffusion properties in biological tissues

The main objective of this work was to develop an application capable of determining the diffusion times and diffusion coefficients of optical clearing agents and water inside a known type of muscle. Different types of chemical agents can also be used with the method implemented, such as medications or metabolic products. Since the diffusion times can be calculated, it is possible to describe the dehydration mechanism that occurs in the muscle. The calculation of the diffusion time of an optical clearing agent allows to characterize the refractive index matching mechanism of optical clearing. By using both the diffusion times and diffusion of water and clearing agents not only the optical clearing mechanisms are characterized, but also information about optical clearing effect duration and magnitude is obtained. Such information is crucial to plan a clinical intervention in cooperation with optical clearing. The experimental method and equations implemented in the developed application are described in throughout this document, demonstrating its effectiveness. The application was developed in MATLAB code, but the method was personalized so it better fits the application needs. This process significantly improved the processing efficiency, reduced the time to obtain he results, multiple validations prevents common errors and some extra functionalities were added such as saving application progress or export information in different formats. Tests were made using glucose measurements in muscle. Some of the data, for testing purposes, was also intentionally changed in order to obtain different simulations and results from the application. The entire project was validated by comparing the calculated results with the ones found in literature, which are also described in this document.