Methodologies for the assessment of industrial and energy assets, based on data analysis and BI

In July 2020, post pandemic onset, Europe launched the Next Generation EU (NGEU) program. The amount of resources deployed to revitalize Europe has reached 750 billion. The NGEU initiative directs significant resources to Italy. These funds can enable our country to boost investment and increase employment. The missions of Italian Recovery and Resilience Plan (PNRR) include digitization, innovation and sustainable mobility (rail network investments, etc.). In this context, this doctorate thesis discusses the importance of infrastructure for society with a special focus on energy, railway and motorway infrastructure. The central theme of sustainability, defined by the World Commission on Environment and Development (WCDE) as ''development that meets the needs of the present generation without compromising the ability of future generations to meet their needs’’, is also highlighted. Through their activities and relationships, organizations contribute positively or negatively to the goal of sustainable development. Sustainability becomes an integrated part of corporate culture. First research in this thesis describes how Artificial Intelligence techniques can play a supporting role for both maintenance operators in tunnel monitoring and those responsible for safety in operation. Relevant information can be extracted from large volumes of data from sensor equipment in an efficient, fast, dynamic and adaptive manner and made immediately usable by those operating machinery and services to support rapid decisions. Performing sensor-based analysis in motorway tunnels represents a major technological breakthrough that would simplify tunnel management activities and thus the detection of possible deterioration, while keeping risk within tolerance limits. The idea involves the creation of an algorithm for detecting faults, acquiring real-time data from tunnel subsystem sensors and using it to help identify the tunnel's state of service. Artificial intelligence models were trained over a sixmonth period with a granularity of one-hour time series measured on a road tunnel forming part of the Italian motorway systems. The verification was carried out with 3 reference to a series of failures recorded by the sensors. The second research argument is relates to the transfer capacities of high-voltage overhead lines (HVOHL), which are often limited by the critical temperature of the power line, which depends on the magnitude of the current transferred and the environmental conditions, i.e. ambient temperature, wind, etc. In order to use existing power lines more effectively (with a view to progressive decarbonization) and more safely with respect to critical power line temperatures, this work proposes a Dynamic Thermal Rating (DTR) approach using IoT sensors installed on a number of HV OHL located in different geographical locations in Italy. The objective is to estimate the temperature and ampacity of the OHL conductor, using a data-driven thermomechanical model with a bayesian probabilistic approach, in order to improve the confidence interval of the results. This work shows that it might be possible to estimate a spatio-temporal temperature distribution for each OHL and an increase in the threshold values of the effective current to optimize the OHL ampacity. The proposed model was validated using the Monte Carlo method. Finally, in this thesis is presented study on KPIs as indispensable allies of top management in the asset control phase. They are often overwhelmed by the availability of a huge amount of Key Performance Indicators (KPIs). Most managers struggle In understanding and identifying the few vital management metrics and instead collect and report a vast amount of everything that is easy to measure. As a result, they end up drowning in data, thirsty for information. This condition does not allow good systems management. The aim of this research is help the Asset Management System (AMS) of a railway infrastructure manager using business intelligence (BI) to equip itself with a KPI management system in line with the AM presented by the normative ISO 55000 - 55001 - 55002 and UIC (International Union of Railways) guideline, for the specific case of a railway infrastructure. This work starts from the study of these regulations, continues with the exploration, definition and use of KPIs. Subsequently KPIs of a generic infrastructure are identified and analyzed, 4 especially for the specific case of a railway infrastructure manager. These KPIs are fitted in the internal elements of the AM frameworks (ISO-UIC) for systematization. Moreover, an analysis of the KPIs now used in the company is made, compared with the KPIs that an infrastructure manager should have. Starting from here a gap analysis is done for the optimization of AMS

A multi-variable DTR algorithm for the estimation of conductor temperature and ampacity on HV overhead lines by IoT data sensors

The transfer capabilities of High-Voltage Overhead Lines (HV OHLs) are often limited by the critical power line temperature that depends on the magnitude of the transferred current and the ambient conditions, i.e., ambient temperature, wind, etc. To utilize existing power lines more effectively (with a view to progressive decarbonization) and more safely with respect to the critical power line temperatures, this paper proposes a Dynamic Thermal Rating (DTR) approach using IoT sensors installed on some HV OHLs located in different Italian geographical locations. The goal is to estimate the OHL conductor temperature and ampacity, using a data-driven thermo-mechanical model with the Bayesian probability approach, in order to improve the confidence interval of the results. This work highlights that it could be possible to estimate a space-time distribution of temperature for each OHL and an increase in the actual current threshold values for optimizing OHL ampacity. The proposed model is validated using the Monte Carlo method

Enabling Railway AM Optimization Using a Rationale KPIs Framework

Often the top management, in the phase of asset controls, finds itself overwhelmed by the availability of a huge amount of Key Performance Indicators (KPIs). Most managers are struggling to understand and identify the vital few management metrics and instead collect and report a vast amount of everything that is easy to measure. As a consequence they end up drowning in data while thirsting for information. This condition does not allow a good management of the systems. The research aim’s is to help the Asset Management System (AMS) of a railway infrastructure manager using Business Intelligence (BI) to have a KPIs management system in line with the principles of AM presented by the normative ISO 55000 - 55001 - 55002 and UIC (International Union of Railways) guideline, for the specific case of a railway infrastructure. This work starts from the study of these regulations, continues with the exploration, definition and use of KPIs. Subsequently KPIs of a generic infrastructure are identified and analyzed , especially for the specific case of a railway infrastructure manager. These KPIs are fitted in the internal elements of the AM frameworks (ISO-UIC) for systematization. Moreover an analysis of the KPIs now used in the company is made, compared with the KPIs that an infrastructure manager should have. Starting from here a Gap Analysis is made for the optimization of AMS

An application of data-driven analysis in road tunnels monitoring

In order to comply with the minimum safety requirements imposed by the Directive 2004/54/EC it is of paramount mportance to correctly manage the operation and maintenance of road tunnels. This research describes how Artificial Intelligence techniques can play a supportive role both for maintenance operators in monitoring tunnels and for safety managers in operation. It is possible to extract relevant information from large volumes of data from sensor equipment in an efficient, fast, dynamic and adaptive way and make it immediately usable by those who manage machinery and servicesto aid quick decisions. Carrying out an analysis based on sensors in motorway tunnels, represents an important technological innovation, which would simplify tunnels management activities and therefore the detection of any possible deterioration, while keeping the risk within tolerance limits. The idea involves the creation of an algorithm for the detection of faults by acquiring data in real time from the sensors of tunnel sub-systems and using them to help identify the service state of the tunnel. The AI models are trained on a period of 6 months with one hour time series granularity measured on a road tunnel part of the Italian motorway systems. The verification has been done with reference to a number of recorded sensor faults

Assessment of Hydrogen and LNG buses adoption as sustainable alternatives to diesel fuel buses in public transportation: Applications to Italian perspective

This work deals with a technical and economical comparison between hydrogen and liquid natural gas (LNG) fueled buses with reference to the standard solution based on diesel fuel internal combustion engines. The level of service is evaluated considering the number of buses replaced and the average kilometers traveled each year for two levels. The economical comparison is made using the Total Cost of Ownership (TCO) method considering capital and operating costs. The costs of LNG and Diesel (at the pump in Italian market) are estimated with reference to the year 2020. Furthermore, an assessment of greenhouse gas emissions will be carried out starting from energy needs, adopting a “cradle to grave” approach, thus evaluating emissions from the well to the tank and from the tank to the wheel. The results show that the operating costs (0.778 €/km) of LNG solution are lower than the Diesel ones (1.072 €/km), while the hydrogen buses can become competitive in the next few .The production of hydrogen with water electrolysis considering the current electricity costs of the Italian market is expensive and involves a cost to the hydrogen pump 7,60 €/kg which makes the operating cost of the hydrogen solution is equal to about 1.420 €/km which makes this solution uncompetitive. It is also important to underline that the cost of green hydrogen production from water electrolysis strongly depends on the cost of electricity. The Life Cicle Analisis (LCA) analysis shows strong environmental benefits of the hydrogen solution in terms of CO2eq if the hydrogen is produced by electrolysis using renewable energy sources. In the other cases, the advantage of using hydrogen is not very strong as it is associated with the use of fossil fuels that release climate-altering substances

A Multi-Variable DTR Algorithm for the Estimation of Conductor Temperature and Ampacity on HV Overhead Lines by IoT Data Sensors

The transfer capabilities of High-Voltage Overhead Lines (HV OHLs) are often limited by the critical power line temperature that depends on the magnitude of the transferred current and the ambient conditions, i.e., ambient temperature, wind, etc. To utilize existing power lines more effectively (with a view to progressive decarbonization) and more safely with respect to the critical power line temperatures, this paper proposes a Dynamic Thermal Rating (DTR) approach using IoT sensors installed on some HV OHLs located in different Italian geographical locations. The goal is to estimate the OHL conductor temperature and ampacity, using a data-driven thermo-mechanical model with the Bayesian probability approach, in order to improve the confidence interval of the results. This work highlights that it could be possible to estimate a space-time distribution of temperature for each OHL and an increase in the actual current threshold values for optimizing OHL ampacity. The proposed model is validated using the Monte Carlo method

Memorie di ricerche antiquarie nella tenuta della «Sepoltura di Nerone» (1780-1796) tratte dall'archivio del Capitolo Vaticano

Viene posta in risalto l'importanza, sul piano documentario (con specifico riferimento alla scienza antiquaria), dell'archivio del Capitolo di S. Pietro, veneranda istituzione, titolare, per secoli, di un vastissimo comprensorio della Campagna Romana. Nel caso della tenuta della Sepoltura di Nerone, la preziosa documentazione archivistica ha contribuito ad un migliore inquadramento delle esplorazioni archeologiche tardo-settecentesche nell'area, fornendo inediti ragguagli in merito alle campagne di scavo, susseguitesi nel periodo 1780-1783 nonché dando notizia di ulteriori (mal conosciuti) interventi in loc