Loredana Zani - Academia.edu (original) (raw)
Papers by Loredana Zani
PubMed, 2003
One of the goals of the EU-Project AMONCO (Advanced Prediction, Monitoring and Controlling of Ana... more One of the goals of the EU-Project AMONCO (Advanced Prediction, Monitoring and Controlling of Anaerobic Digestion Process Behaviour towards Biogas Usage in Fuel Cells) is to create a control tool for the anaerobic digestion process, which predicts the volumetric organic loading rate (Bv) for the next day, to obtain a high biogas quality and production. The biogas should contain a high methane concentration (over 50%) and a low concentration of components toxic for fuel cells, e.g. hydrogen sulphide, siloxanes, ammonia and mercaptanes. For producing data to test the control tool, four 20 l anaerobic Continuously Stirred Tank Reactors (CSTR) are operated. For controlling two systems were investigated: a pure fuzzy logic system and a hybrid-system which contains a fuzzy based reactor condition calculation and a hierachial neural net in a cascade of optimisation algorithms.
Journal of Chemical Technology & Biotechnology, 2003
Due to its intricate internal biological structure the process of anaerobic digestion is difficul... more Due to its intricate internal biological structure the process of anaerobic digestion is difficult to control. The aim of any applied process control is to maximize methane production and minimize the chemical oxygen demand of the effluent and surplus sludge production. Of special interest is the start‐up and adaptation phase of the bioreactor and the recovery of the biocoenose after a toxic event. It is shown that the anaerobic digestion of surplus sludge can be effectively modeled by means of a hierarchical system of neural networks and a prediction of biogas production and composition can be made several time‐steps in advance. Thus it was possible to optimally control the loading rate during the start‐up of a non‐adapted system and to recover an anaerobic reactor after a period of heavy organic overload. During the controlled period an optimal feeding profile that allowed a minimum loading rate of 6 kg COD m−3 d−1 to be maintained was found. Maximum loading rates higher than 12 k...
Environmental Modelling & Software, 2005
The outlook to apply the highly energetic biogas from anaerobic digestion into fuel cells will re... more The outlook to apply the highly energetic biogas from anaerobic digestion into fuel cells will result in a significantly higher electrical efficiency and can contribute to an increase of renewable energy production. The practical bottleneck is the fuel cell poisoning caused by several gaseous trace compounds like hydrogen sulfide and ammonia. Hence artificial neural networks were developed to predict these trace compounds. The experiments concluded that ammonia in biogas can indeed be present up to 93 ppm. Hydrogen sulfide and ammonia concentrations in biogas were modelled successfully using the MATLAB Neural Network Toolbox. A script was developed which made it easy to search for the best neural network models' input/output-parameters, settings and architectures. The models were predicting the trace compounds, even under dynamical conditions. The resulted determination coefficients (R 2) were for hydrogen sulfide 0.91 and ammonia 0.83. Several model predictive control tool strategies were introduced which showed the potential to foresee, control, reduce or even avoid the presence of the trace compounds.
The main purpose of the survey was to collect data on car use, on use of transport modes for long... more The main purpose of the survey was to collect data on car use, on use of transport modes for long distance mobility as well as on some other policy relevant issues (e.g. the attitude towards internalisation of road external costs by means of road charging). The survey involved all the 28 European countries. In each country a sample of 1000 individuals (500 in Cyprus, Luxembourg and Malta) was asked to fill in a questionnaire divided into four sections: a. general information on the respondent (e.g. age, gender, living area) as well as details on availability of cars and public transport service. b. information on everyday mobility in terms of mode used, frequency of trips, duration, distance, inter-modality and opinions on main problems experienced. c. information long distance trips (between 300 km and 1000 km as well as over 1000 km) made in the last 12 months; number of trips by purpose and main mode; connections between rail and air transport. d. opinions on aspects related to t...
Transportation Research Procedia, 2016
The EU-wide survey presented here was carried out in 2014 with the objective of gathering in orde... more The EU-wide survey presented here was carried out in 2014 with the objective of gathering in order a number of transport and mobility indicators on transport user preferences at both urban and long-distance level in a uniform way, with emphasis on the potential of emerging transport technologies and the acceptability of various transport policy measures. The CAWI (Computer Aided Web Interview) survey covered all 28 Member States of the European Union with the same questionnaire translated in the local languages. Samples of 1000 individuals in each country reflected the composition of adult population (from 16 years on) in terms of gender, age class, employment status, education level and living region. The survey provided a rich and comparable picture of mobility across the 28 EU countries; many similarities across countries were found together with some differences. In a way, the findings suggest that, despite some national peculiarities, mobility habits and behaviour are relatively homogenous in Europe and are determined especially by socioeconomic drivers. The result of the survey confirmed that passenger mobility in EU is heavily centred on personal car, which is the most used transport mode also for long distance trips. Relatively higher modal share in East European countries appears to be driven mainly by the lower car availability rather than higher quality of public transport services. Europeans' trips are essentially local, even though there is a share of citizens travelling frequently over longer distances. In particular, individuals with highly qualified jobs travel significantly more than others above 1000 km not only for business but also for leisure.
Technological Forecasting and Social Change, 2014
This study is aimed at building a database of load profiles for electric-drive vehicles (EDVs) ba... more This study is aimed at building a database of load profiles for electric-drive vehicles (EDVs) based on car use profiles in six European countries (Germany, Spain, France, Italy, Poland, and the United Kingdom). Driving profiles were collected by means of sample travel surveys carried out in the six countries. Here, we present the resulting load profiles obtained by associating assumptions on technical features of EDVs and on behavioural elements. The document provides details on the methodology and the assumptions used for driving profiles estimations, discusses the results of a common scenario for six countries and presents an alternative scenario to assess how load profiles might change under alternative parameters and assumptions. The report draws conclusions on this subject, and puts forward suggestions for follow-up studies. As the Commission's in-house science service, the Joint Research Centre's mission is to provide EU policies with independent, evidence-based scientific and technical support throughout the whole policy cycle. Working in close cooperation with policy Directorates-General, the JRC addresses key societal challenges while stimulating innovation through developing new standards, methods and tools, and sharing and transferring its know-how to the Member States and international community. Key policy areas include: environment and climate change; energy and transport; agriculture and food security; health and consumer protection; information society and digital agenda; safety and security including nuclear; all supported through a cross-cutting and multidisciplinary approach.
Water Science and Technology, 2000
In this work the training of a self-organizing map and a feed-forward back-propagation neural net... more In this work the training of a self-organizing map and a feed-forward back-propagation neural network was made. The aim was to model the anaerobic digestion process. To produce data for the training of the neural nets an anaerobic digester was operated at steady state and disturbed by pulsing the organic loading rate. Measured parameters were: gas composition, gas production rate, volatile fatty acid concentration, pH, redox potential, volatile suspended solids and chemical oxygen demand of feed and effluent. It could be shown that both types of self-learning networks in principle could be used to model the process of anaerobic digestion. Using the unsupervised Kohonen self-organizing map, the model's predictions could not follow the measurements in all details. This resulted in an unsatisfactory regression coefficient of R2= 0.69 for the gas composition and R2= 0.76 for the gas production rate. When the supervised FFBP neural net was used the training resulted in more precise p...
PubMed, 2003
One of the goals of the EU-Project AMONCO (Advanced Prediction, Monitoring and Controlling of Ana... more One of the goals of the EU-Project AMONCO (Advanced Prediction, Monitoring and Controlling of Anaerobic Digestion Process Behaviour towards Biogas Usage in Fuel Cells) is to create a control tool for the anaerobic digestion process, which predicts the volumetric organic loading rate (Bv) for the next day, to obtain a high biogas quality and production. The biogas should contain a high methane concentration (over 50%) and a low concentration of components toxic for fuel cells, e.g. hydrogen sulphide, siloxanes, ammonia and mercaptanes. For producing data to test the control tool, four 20 l anaerobic Continuously Stirred Tank Reactors (CSTR) are operated. For controlling two systems were investigated: a pure fuzzy logic system and a hybrid-system which contains a fuzzy based reactor condition calculation and a hierachial neural net in a cascade of optimisation algorithms.
Journal of Chemical Technology & Biotechnology, 2003
Due to its intricate internal biological structure the process of anaerobic digestion is difficul... more Due to its intricate internal biological structure the process of anaerobic digestion is difficult to control. The aim of any applied process control is to maximize methane production and minimize the chemical oxygen demand of the effluent and surplus sludge production. Of special interest is the start‐up and adaptation phase of the bioreactor and the recovery of the biocoenose after a toxic event. It is shown that the anaerobic digestion of surplus sludge can be effectively modeled by means of a hierarchical system of neural networks and a prediction of biogas production and composition can be made several time‐steps in advance. Thus it was possible to optimally control the loading rate during the start‐up of a non‐adapted system and to recover an anaerobic reactor after a period of heavy organic overload. During the controlled period an optimal feeding profile that allowed a minimum loading rate of 6 kg COD m−3 d−1 to be maintained was found. Maximum loading rates higher than 12 k...
Environmental Modelling & Software, 2005
The outlook to apply the highly energetic biogas from anaerobic digestion into fuel cells will re... more The outlook to apply the highly energetic biogas from anaerobic digestion into fuel cells will result in a significantly higher electrical efficiency and can contribute to an increase of renewable energy production. The practical bottleneck is the fuel cell poisoning caused by several gaseous trace compounds like hydrogen sulfide and ammonia. Hence artificial neural networks were developed to predict these trace compounds. The experiments concluded that ammonia in biogas can indeed be present up to 93 ppm. Hydrogen sulfide and ammonia concentrations in biogas were modelled successfully using the MATLAB Neural Network Toolbox. A script was developed which made it easy to search for the best neural network models' input/output-parameters, settings and architectures. The models were predicting the trace compounds, even under dynamical conditions. The resulted determination coefficients (R 2) were for hydrogen sulfide 0.91 and ammonia 0.83. Several model predictive control tool strategies were introduced which showed the potential to foresee, control, reduce or even avoid the presence of the trace compounds.
The main purpose of the survey was to collect data on car use, on use of transport modes for long... more The main purpose of the survey was to collect data on car use, on use of transport modes for long distance mobility as well as on some other policy relevant issues (e.g. the attitude towards internalisation of road external costs by means of road charging). The survey involved all the 28 European countries. In each country a sample of 1000 individuals (500 in Cyprus, Luxembourg and Malta) was asked to fill in a questionnaire divided into four sections: a. general information on the respondent (e.g. age, gender, living area) as well as details on availability of cars and public transport service. b. information on everyday mobility in terms of mode used, frequency of trips, duration, distance, inter-modality and opinions on main problems experienced. c. information long distance trips (between 300 km and 1000 km as well as over 1000 km) made in the last 12 months; number of trips by purpose and main mode; connections between rail and air transport. d. opinions on aspects related to t...
Transportation Research Procedia, 2016
The EU-wide survey presented here was carried out in 2014 with the objective of gathering in orde... more The EU-wide survey presented here was carried out in 2014 with the objective of gathering in order a number of transport and mobility indicators on transport user preferences at both urban and long-distance level in a uniform way, with emphasis on the potential of emerging transport technologies and the acceptability of various transport policy measures. The CAWI (Computer Aided Web Interview) survey covered all 28 Member States of the European Union with the same questionnaire translated in the local languages. Samples of 1000 individuals in each country reflected the composition of adult population (from 16 years on) in terms of gender, age class, employment status, education level and living region. The survey provided a rich and comparable picture of mobility across the 28 EU countries; many similarities across countries were found together with some differences. In a way, the findings suggest that, despite some national peculiarities, mobility habits and behaviour are relatively homogenous in Europe and are determined especially by socioeconomic drivers. The result of the survey confirmed that passenger mobility in EU is heavily centred on personal car, which is the most used transport mode also for long distance trips. Relatively higher modal share in East European countries appears to be driven mainly by the lower car availability rather than higher quality of public transport services. Europeans' trips are essentially local, even though there is a share of citizens travelling frequently over longer distances. In particular, individuals with highly qualified jobs travel significantly more than others above 1000 km not only for business but also for leisure.
Technological Forecasting and Social Change, 2014
This study is aimed at building a database of load profiles for electric-drive vehicles (EDVs) ba... more This study is aimed at building a database of load profiles for electric-drive vehicles (EDVs) based on car use profiles in six European countries (Germany, Spain, France, Italy, Poland, and the United Kingdom). Driving profiles were collected by means of sample travel surveys carried out in the six countries. Here, we present the resulting load profiles obtained by associating assumptions on technical features of EDVs and on behavioural elements. The document provides details on the methodology and the assumptions used for driving profiles estimations, discusses the results of a common scenario for six countries and presents an alternative scenario to assess how load profiles might change under alternative parameters and assumptions. The report draws conclusions on this subject, and puts forward suggestions for follow-up studies. As the Commission's in-house science service, the Joint Research Centre's mission is to provide EU policies with independent, evidence-based scientific and technical support throughout the whole policy cycle. Working in close cooperation with policy Directorates-General, the JRC addresses key societal challenges while stimulating innovation through developing new standards, methods and tools, and sharing and transferring its know-how to the Member States and international community. Key policy areas include: environment and climate change; energy and transport; agriculture and food security; health and consumer protection; information society and digital agenda; safety and security including nuclear; all supported through a cross-cutting and multidisciplinary approach.
Water Science and Technology, 2000
In this work the training of a self-organizing map and a feed-forward back-propagation neural net... more In this work the training of a self-organizing map and a feed-forward back-propagation neural network was made. The aim was to model the anaerobic digestion process. To produce data for the training of the neural nets an anaerobic digester was operated at steady state and disturbed by pulsing the organic loading rate. Measured parameters were: gas composition, gas production rate, volatile fatty acid concentration, pH, redox potential, volatile suspended solids and chemical oxygen demand of feed and effluent. It could be shown that both types of self-learning networks in principle could be used to model the process of anaerobic digestion. Using the unsupervised Kohonen self-organizing map, the model's predictions could not follow the measurements in all details. This resulted in an unsatisfactory regression coefficient of R2= 0.69 for the gas composition and R2= 0.76 for the gas production rate. When the supervised FFBP neural net was used the training resulted in more precise p...