Influence of alternative electron acceptors on the anaerobic biodegradation of polycyclic aromatic hydrocarbons

Polycyclic aromatic hydrocarbons are chemicals produced by both human activities and natural sources and they have been present in the biosphere since millions of years. For this reason microorganisms should have developed, during the world history, the capacity of metabolized them under different electron acceptors and redox conditions. The deep understanding of these natural attenuation processes and of microbial degradation pathways has a main importance in the cleanup of contaminated areas. Anaerobic degradation of aromatic hydrocarbons is often presumed to be slow and of a minor ecological significance compared with the aerobic processes; however anaerobic bioremediation may play a key role in the transformation of organic pollutants when oxygen demand exceeds supply in natural environments. Under such conditions, anoxic and anaerobic degradation mediated by denitrifying or sulphate-reducing bacteria can become a key pathway for the contaminated lands clean up. Actually not much is known about anaerobic bioremediation processes. Anaerobic biodegrading techniques may be really interesting for the future, because they give the possibility of treating contaminated soil directly in their natural status, decreasing the costs concerning the oxygen supply, which usually are the highest ones, and about soil excavations and transports in appropriate sites for a further disposal. The aim of this dissertation work is to characterize the conditions favouring the anaerobic degradation of polycyclic aromatic hydrocarbons. Special focus will be given to the assessment of the various AEA efficiency, the characterization of degradation performance and rates under different redox conditions as well as toxicity monitoring. A comparison with aerobic and anaerobic degradation concerning the same contaminated material is also made to estimate the different biodegradation times.

Design of alternative energy storage systems for hybrid vehicles based on statistical processing of driving cycles information

Hybrid vehicles represent the future for automakers, since they allow to improve the fuel economy and to reduce the pollutant emissions. A key component of the hybrid powertrain is the Energy Storage System, that determines the ability of the vehicle to store and reuse energy. Though electrified Energy Storage Systems (ESS), based on batteries and ultracapacitors, are a proven technology, Alternative Energy Storage Systems (AESS), based on mechanical, hydraulic and pneumatic devices, are gaining interest because they give the possibility of realizing low-cost mild-hybrid vehicles. Currently, most literature of design methodologies focuses on electric ESS, which are not suitable for AESS design. In this contest, The Ohio State University has developed an Alternative Energy Storage System design methodology. This work focuses on the development of driving cycle analysis methodology that is a key component of Alternative Energy Storage System design procedure. The proposed methodology is based on a statistical approach to analyzing driving schedules that represent the vehicle typical use. Driving data are broken up into power events sequence, namely traction and braking events, and for each of them, energy-related and dynamic metrics are calculated. By means of a clustering process and statistical synthesis methods, statistically-relevant metrics are determined. These metrics define cycle representative braking events. By using these events as inputs for the Alternative Energy Storage System design methodology, different system designs are obtained. Each of them is characterized by attributes, namely system volume and weight. In the last part the work, the designs are evaluated in simulation by introducing and calculating a metric related to the energy conversion efficiency. Finally, the designs are compared accounting for attributes and efficiency values. In order to automate the driving data extraction and synthesis process, a specific script Matlab based has been developed. Results show that the driving cycle analysis methodology, based on the statistical approach, allows to extract and synthesize cycle representative data. The designs based on cycle statistically-relevant metrics are properly sized and have satisfying efficiency values with respect to the expectations. An exception is the design based on the cycle worst-case scenario, corresponding to same approach adopted by the conventional electric ESS design methodologies. In this case, a heavy system with poor efficiency is produced. The proposed new methodology seems to be a valid and consistent support for Alternative Energy Storage System design.