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The current energy scenario requires that gas turbines (GTs) operate at their maximum efficiency and highest reliability. Trip is one of the most disrupting events that reduces GT availability and increases maintenance costs. To tackle the challenge of GT trip prediction, this paper presents a methodology that has the goal of monitoring the early warnings raised during GT operation and trigger an alert to avoid trip occurrence. The methodology makes use of an auto-encoder (prediction model) and a three-stage criterion (detection procedure). The auto-encoder is first trained to reconstruct safe operation data and subsequently tested on new data collected before trip occurrence. The trip detection criterion checks whether the individually tested data points should be classified as normal or anomalous (first stage), provides a warning if the anomaly score over a given time frame exceeds a threshold (second stage), and, finally, combines consecutive warnings to trigger a trip alert in advance (third stage). The methodology is applied to a real-world case study composed of a collection of trips, of which the causes may be different, gathered from various GTs in operation during several years. Historical observations of gas path measurements taken during three days of GT operation before trip occurrence are employed for the analysis. Once optimally tuned, the methodology provides a trip alert with a reliability equal to 75% at least 10 h in advance before trip occurrence. [ABSTRACT FROM AUTHOR]

One of the most disrupting events that affect gas turbine (GT) operation is trip, since its occurrence reduces machine life span and also causes business interruption. Thus, early detection of incipient symptoms of GT trip is crucial to ensure efficient operation and save costs. This paper presents a data-driven methodology of which the goal is the disclosure of the onset of trip symptoms by exploring multiple trigger scenarios. For each scenario, a time window of the same length is considered before and after the trigger time point: the former is supposed to be representative of normal operation and is labeled "no trip," whereas the latter is labeled "trip." A long short-term memory (LSTM) neural network is first trained for each scenario and subsequently tested on new trips over a timeframe of 3 days of operation before trip occurrence. Finally, trips are clustered into homogeneous groups according to their most likely trigger position, which identifies the time point of onset of trip symptoms. The methodology is applied to two real-world case studies composed of a collection of trips, of which the causes are different, taken from various fleets of GTs in operation. Data collected from multiple sensors are employed and analyzed. The methodology provides the most likely trigger position for four clusters of trips and both case studies with a confidence in the range 66-97%. [ABSTRACT FROM AUTHOR]

In the field of gas turbine (GT) monitoring and diagnostics, GT trip is of great concern for manufactures and users. In fact, due to the number of issues that may cause a trip, its occurrence is not infrequent, and its prediction is a quite unexplored field of research. This is demonstrated by the fact that, despite its relevance, a comprehensive study on the reliability of predicting GT trip has not been proposed yet. To fill this gap, this paper investigates the fusion of five data-driven base models by means of voting and stacking, in order to improve prediction accuracy and robustness. The five benchmark supervised machine learning and deep learning classifiers are k-nearest neighbors, support vector machine (SVM), Naïve Bayes (NB), decision trees (DTs), and long short-term memory (LSTM) neural networks. While voting just averages the predictions of base models, without providing additional pieces of information, stacking is a technique used to aggregate heterogeneous models by training an additional machine learning model (namely, stacked ensemble model) on the predictions of the base models. The analyses carried out in this paper employ filed observations of both safe operation and trip events, derived from a large fleet of industrial Siemens GTs in operation. The results demonstrate that the stacked model provides higher accuracy than base models and also outperforms voting by proving more effective, especially when the reliability of the prediction of base models is poor. [ABSTRACT FROM AUTHOR]

A gas turbine trip is an unplanned shutdown, of which the most relevant consequences are business interruption and a reduction of equipment remaining useful life. Thus, understanding the underlying causes of gas turbine trip would allow predicting its occurrence in order to maximize gas turbine profitability and improve its availability. In the ever competitive Oil & Gas sector, data mining and machine learning are increasingly being employed to support a deeper insight and improved operation of gas turbines. Among the various machine learning tools, random forests are an ensemble learning method consisting of an aggregation of decision tree classifiers. This paper presents a novel methodology aimed at exploiting information embedded in the data and develops random forest models, aimed at predicting gas turbine trip based on information gathered during a timeframe of historical data acquired from multiple sensors. The novel approach exploits time series segmentation to increase the amount of training data, thus reducing overfitting. First, data are transformed according to a feature engineering methodology developed in a separate work by the same authors. Then, Random Forest models are trained and tested on unseen observations to demonstrate the benefits of the novel approach. The superiority of the novel approach is proved by considering two real-word case studies, involving field data taken during three years of operation of two fleets of gas turbines located in different regions. The novel methodology allows values of precision, recall and accuracy in the range 75-85%, thus demonstrating the industrial feasibility of the predictive methodology. [ABSTRACT FROM AUTHOR]

Residual feed intake (RFI) is a measure of feed efficiency (FE). Differences in plasma metabolites concentration found in animals with contrasting RFI are potential biomarkers of FE. Furthermore, feed behavior patterns could also be used to identify animals with contrasting RFI. We hypothesize that the integration of plasma metabolomics data and feed behavior can predict the future animal performance. We used 302 bulls subjected to an ad libitum diet for 56-d while allocated in pens equipped with GrowSafe System to evaluate RFI. Blood samples were collected for metabolomics analysis at the beginning (d 0) and at the end (d 56) of the trial. Feeding behavior metrics including feeding bout (FB), and eating time were estimated. Subsequently, the bulls were classified as low RFI (LRFI) and high RFI (HRFI). The performance test showed no differences between LRFI and HRFI animals in the initial body weight (LRFI: 408.62 ± 47.43 Kg; HRFI: 407.64 ± 60.22; P = 0.41), final body weight (LRFI: 510.33 ± 58.28; HRFI: 500.82 ± 72.23; P = 0.46), and average daily gain (LRFI: 1.67 ± 0.28; HRFI: 1.54 ± 0.36; P = 0.42). But the dry matter intake was less for LRFI (6.45 ± 1.78) in comparison with HRFI animals (8.81 ± 2.63; P < 0.001). Metabolomics analysis performed on the top and bottom 30 bulls based on RFI, reveled increased concentrations of 18 metabolites in LRFI bulls on d 0, and 12 different metabolites on d 56. Among these compounds, Cer(d18:1/23:0) level was statistically greater on both d 0 (LRFI: 0.233 ± 0.016 µM; HRFI: 0.187 ± 0.014 µM, P = 0.039) and d 56 (LRFI: 0.267 ± 0.016 µM; HRFI: 0.205 ± 0.013 µM, P = 0.02), emerging as a potential biomarker distinguished by the multivariate analysis, capable of discerning between LRFI and HRFI animals. On the other hand, FB (LRFI: 13 events/d; HRFI: 15 events/d; P = 0.019) and eating time LRFI: 119 min/d; HRFI: 153 min/d; P < 0.001) were different between the two groups. The more efficient animals (LRFI) were observed to visit the feed bunk less frequently and spend less time consuming feed compared with the less efficient animals (HRFI). In conclusion, further exploration of the metabolites' pathways and their roles and recognition of feeding behavior patterns could provide valuable insights into the mechanisms contributing to FE. [ABSTRACT FROM AUTHOR]

At present, the challenges related to energy market force gas turbine owners to improve the reliability and availability of gas turbine engines, especially in the ever competitive Oil and Gas sector. Gas turbine trip leads to business interruption and also reduces equipment remaining useful life. Thus, the identification of symptoms of trips allows the prediction of their occurrence and avoids further damages and costs. Gas turbine transients are tracked by gas turbine operators while they occur, but a database including the complete details of past events for many fleets of engines is not always available. Therefore, a methodology aimed at classifying transients into clusters that identify the type of event (e.g., normal shutdown or trip) is required. Clustering is a data mining technique that addresses the scope of partitioning multivariate time series (MTS) into a given number of homogeneous and separated groups. Thus, the multivariate time series belonging to the same cluster are expected to be very similar to each other. This paper presents a structured methodology composed of a subsequent matching algorithm, a featured-based clustering approach exploiting the unsupervised fuzzy C-means algorithm and a procedure that assigns a label to each cluster for classification purposes. The methodology is applied to a real-word case-study that includes transients acquired from a fleet of Siemens gas turbines in operation during 3 years. The results obtained by using heterogeneous datasets including six measured variables allowed values of Precision, Recall and Accuracy higher than 90% in almost all cases. [ABSTRACT FROM AUTHOR]

Sensor fault detection and classification is a key challenge for machine monitoring and diagnostics, since raw data cleaning represents a key process in the gas turbine industry. To this end, this paper presents a comprehensive approach for detection, classification, and integrated diagnostics of gas turbine sensors (named DCIDS), which was previously developed by the authors and has been substantially improved and validated by means of field data. For a single sensor or redundant/correlated sensors, the improved diagnostic tool, called improved-DCIDS (I-DCIDS), can identify seven classes of faults, i.e., out of range, stuck signal, dithering, standard deviation, trend coherence, spike, and bias. First, this paper details the I-DCIDS methodology for sensor fault detection and classification. The methodology uses basic mathematical laws that require some user-defined configuration parameters, i.e., acceptability thresholds and windows of observation. Second, a sensitivity analysis is carried out on I-DCIDS parameters to derive some rules of thumb about their optimal setting. The sensitivity analysis is performed on four heterogeneous and challenging datasets with redundant sensors acquired from Siemens gas turbines (GTs). The results demonstrate the diagnostic capability of the I-DCIDS approach in a real-world scenario. Moreover, the methodology proves to be suitable for all types of datasets and physical quantities and, thanks to its optimal tuning, can also identify the exact time point of fault onset. [ABSTRACT FROM AUTHOR]

Nowadays, gas turbines (GTs) are equipped with an increasing number of sensors, of which the acquired data are used for monitoring and diagnostic purposes. Therefore, anomaly detection in sensor time series is a crucial aspect for raw data cleaning, in order to identify accurate and reliable data. To this purpose, a novel methodology based on Bayesian hierarchical models (BHMs) is proposed in this paper. The final aim is the exploitation of information held by a pool of observations from redundant sensors as knowledge base to generate statistically consistent measurements according to input data. In this manner, it is possible to simulate a "virtual" healthy sensor, also known as digital twin, to be used for sensor fault identification. The capability of the novel methodology based on BHM is assessed by using field data with two types of implanted faults, i.e., spikes and bias faults. The analyses consider different numbers of faulty sensors within the pool and different fault magnitudes. In this manner, different levels of fault severity are investigated. The results demonstrate that the new approach is successful in most fault scenarios for both spike and bias faults and provide guidelines to tune the detection criterion based on the morphology of the available data. [ABSTRACT FROM AUTHOR]

The prediction of time evolution of gas turbine (GT) performance is an emerging requirement of modern prognostics and health management (PHM), aimed at improving system reliability and availability, while reducing life cycle costs. In this work, a data-driven Bayesian hierarchical model (BHM) is employed to perform a probabilistic prediction of GT future behavior, thanks to its capability to deal with fleet data from multiple units. First, the theoretical background of the predictive methodology is outlined to highlight the inference mechanism and data processing for estimating BHM-predicted outputs. Then, the BHM approach is applied to both simulated and field data representative of GT degradation to assess its prediction reliability and grasp some rules of thumb for minimizing BHM prediction error. For the considered field data, the average values of the prediction errors are found to be lower than 1.0% or 1.7% for single- or multi-step prediction, respectively. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of forecasting the future values of gas turbine measureable quantities. The final aim is the simulation of "virtual sensors" capable of producing statistically coherent measurements aimed at replacing anomalous observations discarded from the time series. Among the different available approaches, the Bayesian forecasting method (BFM) adopted in this paper uses the information held by a pool of observations as knowledge base to forecast the values at a future state. The BFM algorithm is applied in this paper to Siemens field data to assess its prediction capability, by considering two different approaches, i.e., single-step prediction (SSP) and multistep prediction (MSP). While SSP predicts the next observation by using true data as base of knowledge, MSP uses previously predicted data as base of knowledge to perform the prediction of future time steps. The results show that BFM single-step average prediction error can be very low, when filtered field data are analyzed. On the contrary, the average prediction error achieved in case of BFM multistep prediction is remarkably higher. To overcome this issue, the BFM single-step prediction scheme is also applied to clusters of time-wise averaged data. In this manner, an acceptable average prediction error can be achieved by considering clusters composed of 60 observations. [ABSTRACT FROM AUTHOR]

Highlights • Set-up of a physics-based model to predict the performance curves of pumps as turbines. • The model is calibrated on experimental data from four centrifugal pumps taken from literature. • The predicted performance curves are physically consistent over the entire range of operation. • Model prediction error is acceptable, compared to other methods available in literature. • The physics-based model is powerful and reliable to estimate PAT complete performance curves. Abstract This paper presents the development of a physics-based simulation model, aimed at predicting the performance curves of pumps as turbines (PATs) based on the performance curves of the respective pump. The simulation model implements the equations to be used for the estimation of head, power and efficiency for both direct and reverse operation. Model tuning on a given machine is performed by using loss coefficients and specific parameters identified by means of an optimization procedure, which is first applied to the considered pumps and subsequently to the same machine running in PAT mode. The simulation model is calibrated on data taken from literature, reporting both pump and PAT performance curves for head, power and efficiency over the entire range of operation. The performance data were acquired experimentally from four different centrifugal pumps, running in both pump and PAT mode and characterized by specific speed values in the range of 1.53–5.82. The accuracy of the predictions of the physics-based simulation model is quantitatively assessed against both pump and PAT experimental performance curves. Prediction consistency from a physical point of view is also evaluated. The results presented in this paper highlight that all the performance curves predicted by the simulation model are physically consistent over the entire range of operation. In general, the prediction error on the head of PATs is acceptable, while the accuracy of the prediction of PAT power, and thus of PAT efficiency, is more case-sensitive and usually higher. The relative deviation of model prediction with respect to the field data regarding head and power at the PAT best efficiency point always seems acceptable compared to the uncertainty of the original experimental data and to typical deviations of other methods available in literature. As a conclusion, the physics-based simulation model developed in this paper represents a powerful and reliable tool for estimating PAT performance curves over the entire range of operation based on pump characteristics. [ABSTRACT FROM AUTHOR]

In this study, we present the results of a two-dimensional fluid-dynamic simulation of novel rotor geometry with spline function which is derivative of the traditional S-shape Savonius blade. A computational fluid dynamic (CFD) analysis is conducted using the Spalart-Allmaras turbulent model, validated using experimental data released by Sandia National Laboratory. Results are presented in terms of dimensionless torque and power coefficients, assuming a wind speed of 7 m/s and height and rotor diameter of 1m. Furthermore, analysis of the forces acting on the rotor is conducted by evaluating frontal and side forces on each blade, and the resultant force acting on the central shaft. A qualitative representation of the vorticity around the traditional and spline rotor is shown to prove that the novel blade allows less turbulent flow through the rotor. [ABSTRACT FROM AUTHOR]

This paper deals with the estimation of the energy production by means of pumps used as turbines to exploit residual hydraulic energy, as in the case of available head and flow rate in water distribution networks. To this aim, four pumps with different characteristics are investigated to estimate the producible yearly electric energy. The performance curves of Pumps As Turbines (PATs), which relate head, power, and efficiency to the volume flow rate over the entire PAT operation range, were derived by using published experimental data. The four considered water distribution networks, for which experimental data taken during one year were available, are characterized by significantly different hydraulic features (average flow rate in the range 10-116 L/s; average pressure reduction in the range 12-53 m). Therefore, energy production accounts for actual flow rate and head variability over the year. The conversion efficiency is also estimated, for both the whole water distribution network and the PAT alone. [ABSTRACT FROM AUTHOR]

After a 50-year armed conflict, negotiations with guerrilla groups are likely to lead to peace agreements in Colombia. A post-conflict context would open up the possibility of modernizing agriculture, improving living standards in rural areas and making good use of the vast natural resources. Sustainable bioenergy combined with improved land use strategies is of particular interest in this context. However, while bioenergy is today the second largest renewable resource after hydropower, no official plans exist for exploiting it in a post-conflict context. Our study investigates the impacts that an accelerated deployment of bioenergy could have in Colombia until 2030, under different land use pathways. Firstly, we review the country's socioeconomic, land use, energy and emissions context. Then, we identify lessons that Colombia could learn from Brazil to accomplish the proposed targets. Secondly, we explore various scenarios deploying different technologies (bioethanol, biodiesel, renewable diesel, biomethane and biomass-based power generation & CHP) and land use pathways that are likely to be implemented in a post-conflict scenario (zero deforestation, agricultural intensification and extensification). Thirdly, we analyze variations in energy demand and supply, greenhouse gas emissions, land use change and biofuel trade. We find that biomethane and biomass-based power generation & CHP could reduce emissions more effectively than first-generation biofuels. However, their abatement is only 5% relative to a baseline scenario. Combining all bioenergy technologies with zero deforestation, agricultural intensification and extensification could boost abatements up to 280%, a value four times higher than the national commitments by 2030. Our study shows that relatively simpler land use and energy models using free software can produce results of quality comparable to more complex and widely accepted models (e.g. IAMs). These results might be helpful to policymakers evaluating the role of bioenergy in a post-conflict context and to other developing countries with significant bioenergy potential and similar conditions. [ABSTRACT FROM AUTHOR]

This paper describes the process of developing a technology roadmap for deploying bioenergy technologies at a country level. A method for energy technology roadmapping adapted to the conditions of developing countries is proposed. This method combines an acknowledged roadmapping framework from prior art, a new strategy to build consensus based on the Delphi method and a strong focus on analytical modeling for supporting expert judgment. This method aims to be simple, transparent and affordable. The proposed method is applied to Colombia for creating a plan to deploy sustainable bioenergy technologies in Colombia until 2030. This plan consists of a set of long-term goals, milestones, barriers and action items identified by over 30 experts for key bioenergy technology areas (viz. bioethanol, biodiesel, renewable diesel, biomethane, biogas, waste-to-energy and power generation and combined heat and power). Finally, the relevance of the process of developing a technology roadmap for bioenergy exploitation in Colombia in other developing countries is discussed. [ABSTRACT FROM AUTHOR]

This paper presents a modeling framework to address the energy, economy, emissions and land use nexus when exploiting bioenergy in developing countries. The modeling framework combines a qualitative and a quantitative element. The qualitative element integrates two components: (1) technology roadmapping to identify long-term technology targets through expert judgment and (2) scenario analysis to investigate different future storylines. The quantitative element comprises four integrated tools, namely the energy system model (ESM), the land use and trade model (LUTM), an economic model, and an external climate model. An overview of the modeling framework, scenario analysis, structure of the models, modeling techniques, mathematical formulations and assumptions is presented and discussed. The modeling framework is applied to the particular context of Colombia, as a case study of a developing country with large bioenergy potential. In this study case, the impacts that an accelerated deployment of bioenergy technologies might cause on the energy demand and supply, emissions and land use until 2030 are evaluated. Results suggest that a plan to exploit bioenergy in Colombia should prioritize the deployment of technologies for biomethane production, power generation & CHP, which can reduce more GHG emissions and more emissions per incremental hectare of land than first-generation biofuels. Moreover, while the share of bioenergy in the primary energy demand decreases in all the analyzed scenarios, it is possible to envision significant increases in the share of bioenergy in road transport energy demand, power generation and natural gas supply for scenarios implementing roadmap goals. In addition, impacts of El Niño oscillation on the dependence of hydro for power generation can be partly mitigated by exploiting the complementarity of hydro and bioenergy, which might result in a reduction of up to 5–6% in the demand for fossil fuels used in power generation in dry years. However, despite the ambitious goals proposed here, bioenergy alone cannot significantly reduce emissions by 2030 (maximum 10% reduction relative to baseline) and effective climate change mitigation requires a portfolio of additional measures. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to address uncertainty and improve reliability of the estimation of the biomass energy potential at a country level, particularly suitable for situations when quality and availability of data are limited. The proposed methodology improves the prediction reliability by following four steps: 1) using a simple accounting framework, 2) using a robust selection of probability density functions, 3) using a probabilistic propagation of uncertainty and 4) using sensitivity analysis to identify key variables contributing to uncertainty as well as a root cause analysis and a set of sub-models to improve estimation of key variables. The application of the methodology to the energy scenario in Colombia shows that the improved estimation of the theoretical energy potential has an almost identical mean value compared to the preliminary estimate, but the uncertainty is significantly lower (less than 50%). Moreover, the mean value of the technical energy potential obtained through the methodology is 25% lower than the preliminary potential and the uncertainty reduces by one third. [ABSTRACT FROM AUTHOR]

This study deals with modeling and simulation of the transient behavior of an Industrial Power Plant Gas Turbine (IPGT). The data used for model setup and validation were taken experimentally during the start-up procedure of a single-shaft heavy duty gas turbine. Two different models are developed and compared by using both a physics-based and a black-box approach, and are implemented by using the matlab© tools including Simulink and Neural Network toolbox, respectively. The Simulink model was constructed based on the thermodynamic and energy balance equations in matlab environment. The nonlinear autoregressive with exogenous inputs NARX model was set up by using the same data sets and subsequently applied to each of the data sets separately. The results showed that both Simulink and NARX models are capable of satisfactory prediction, if it is considered that the data used for model training and validation is experimental data taken during gas turbine normal operation by using its standard instrumentation. [ABSTRACT FROM AUTHOR]

In this paper, a prognostic methodology is applied to gas turbine field data to assess its capability as a predictive tool for degradation effects. On the basis of the recordings of past behavior, the methodology provides a prediction of future performance, i.e., the probability that degradation effects are at an acceptable level in future operations. The analyses carried out in this paper consider two different parameters (power output and compressor efficiency) of three different Alstom gas turbine power plants (gas turbine type GT13E2, GT24, and GT26). To apply the prognostic methodology, site specific degradation threshold values were defined, to identify the time periods with acceptable degradation (i.e., higher-than-threshold operation) and the time periods where maintenance activities are recommended (i.e., lower-than-threshold operation). This paper compares the actual distribution of the time points until the degradation limit is reached (discrete by nature) to the continuously varying distribution of the time points simulated by the probability density functions obtained through the prognostic methodology. Moreover, the reliability of the methodology prediction is assessed for all the available field data of the three gas turbines and for two values of the threshold. For this analysis, the prognostic methodology is applied by considering different numbers of degradation periods for methodology calibration and the accuracy of the next forecasted trends is compared to the real data. Finally, this paper compares the prognostic methodology prediction to a "purely deterministic" prediction chosen to be the average of the past time points of higher-than-threshold operations. The results show that, in almost all cases, the prognostic methodology allows a better prediction than the "purely deterministic" approach for both power and compressor efficiency degradation. Therefore, the prognostic methodology seems to be a robust and reliable tool to predict gas turbine power plant "probabilistic" degradation. [ABSTRACT FROM AUTHOR]

Bovine viral diarrhea virus (BVDV) infects ruminants, with a worldwide distribution, the virus causes a broad spectrum of clinical diseases and economic losses. Vaccination against BVDV is an important component of prevention and control programs. Currently, only modified live vaccines (MLV) and inactivated vaccines are used. Both have historical disadvantages; MLV in terms of safety and inactivated vaccines in terms of immunogenicity. We have previously reported the development and efficacy trials of the first targeted subunit vaccine against BVDV. The core of the vaccine is a fusion of the BVDV structural protein, E2, to a singlechain antibody, APCH, together termed, APCH-E2. The APCH antibody targets the E2 antigen to the MHC-II present on antigen-presenting cells. The goal of this work was to evaluate passive immunity through colostrum and active immunity of calves immunized with the novel vaccine. 24 A. angus heifers were divided into two groups, 12 immunized with the vaccine and 12 received placebo, in the last trimester of gestation. Serum samples from calves were taken at day 30 of age and analyzed by competitive ELISA. In the vaccinated group, 92% of the d30 old calves maintained medium (25%) or high (67%) antibody levels against BVDV, while 50% of the animals in the control group presented low antibody level (Pearson's chi-squared p:0,07) (Table 1). At 5 months of age, calves of both groups were immunized with the targeted vaccine. Thirty days later, 96% of the calves had medium or high antibody levels against BVDV, which was independent of their respective heifer vaccine status. These results confirm the new targeted subunit vaccine against BVDV is safe and efficacious to be used in pregnant cattle and can passively immunize newborn calves. They also show that these maternal antibodies do not interfere with the active immunization of calves. [ABSTRACT FROM AUTHOR]

The performance of gas turbines degrades over time and, as a consequence, a decrease in gas turbine performance parameters also occurs, so that they may fall below a given threshold value. Therefore, corrective maintenance actions are required to bring the system back to an acceptable operating condition. In today's competitive market, the prognosis of the time evolution of system performance is also recommended, in such a manner as to take appropriate action before any serious malfunctioning has occurred and, as a consequence, to improve system reliability and availability. Successful prognostics should be as accurate as possible, because false alarms cause unnecessary maintenance and nonprofitable stops. For these reasons, a prognostic methodology, developed by the authors, is applied in this paper to assess its prediction reliability for several degradation scenarios typical of gas turbine performance deterioration. The methodology makes use of the Monte Carlo statistical method to provide, on the basis of the recordings of past behavior, a prediction of future availability, i.e., the probability that the considered machine of component can be found in the operational state at a given time in the future. The analyses carried out in this paper aim to assess the influence of the degradation scenario on methodology prediction reliability, as a function of a user-defined threshold and minimum value allowed for the parameter under consideration. A technique is also presented and discussed, in order to improve methodology prediction reliability by means a correction factor applied to the time points used for methodology calibration. The results presented in this paper show that, for all the considered degradation scenarios, the prediction error is lower than 4% (in most cases, it is even lower than 2%), if the availability is estimated for the next trend, while it is not higher than 12%, if the availability is estimated five trends ahead. The application of a proper correction factor allows the prediction errors after five trends to be reduced to approximately 5%. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a procedure for Risk Assessment, which makes use of two risk indices (PML – Probable Maximum Loss and MFL – Maximum Foreseeable Loss) is applied to power plants to evaluate potential economic losses due to risk exposure for two different loss scenarios (probable and worst-case). First, the procedure is presented in order to provide a prediction of probable and maximum loss as a function of power output. Second, the economic loss predicted through the risk assessment procedure is compared to real power plant loss values, taken from published data, to validate the methodology against field data. Finally, the procedure is applied to some combined-cycle power plants by also including the contribution of business interruption losses. In this manner, the most common sources of loss are identified. A sample analysis for risk/benefit evaluation is also carried out to estimate whether the adoption of risk assessment procedures allows an actual profit for plant owners. Customer feedback is also discussed. The results presented in this paper allow a practical and easy-to-use rule of thumb for loss estimation and a guideline for plant owners to evaluate the impact of risk assessment procedures on operational costs. [Copyright &y& Elsevier]

This paper deals with the comparison of different methods which can be used for the prediction of the performance curves of pumps as turbines (PATs). The considered approaches are four, i.e., one physics-based simulation model (“white box” model), two “gray box” models, which integrate theory on turbomachines with specific data correlations, and one “black box” model. More in detail, the modeling approaches are: (1) a physics-based simulation model developed by the same authors, which includes the equations for estimating head, power, and efficiency and uses loss coefficients and specific parameters; (2) a model developed by Derakhshan and Nourbakhsh, which first predicts the best efficiency point of a PAT and then reconstructs their complete characteristic curves by means of two ad hoc equations; (3) the prediction model developed by Singh and Nestmann, which predicts the complete turbine characteristics based on pump shape and size; (4) an Evolutionary Polynomial Regression model, which represents a data-driven hybrid scheme which can be used for identifying the explicit mathematical relationship between PAT and pump curves. All approaches are applied to literature data, relying on both pump and PAT performance curves of head, power, and efficiency over the entire range of operation. The experimental data were provided by Derakhshan and Nourbakhsh for four different turbomachines, working in both pump and PAT mode with specific speed values in the range 1.53–5.82. This paper provides a quantitative assessment of the predictions made by means of the considered approaches and also analyzes consistency from a physical point of view. Advantages and drawbacks of each method are also analyzed and discussed. [ABSTRACT FROM AUTHOR]

Gas turbine trip is an operational event that arises when undesirable operating conditions are approached or exceeded, and predicting its onset is a largely unexplored area. The application of novel artificial intelligence methods to this problem is interesting both from the computer science and the engineering point of view, and the results may be relevant in both the academia and the industry. In this paper, we consider data gathered from a fleet of Siemens industrial gas turbines in operation that includes several thermodynamic variables observed during a long period of operation. To assess the possibility of predicting trip events, we first apply a new, systematic statistical analysis to identify the most important variables, then we use a novel machine learning technique known as temporal decision tree, which differs from canonical decision tree because it allows a native treatment of the temporal component, and has an elegant logical interpretation that eases the posthoc validation of the results. Finally, we use the learned models to extract statistical rules. As a result, we are able to select the five most informative variables, build a predictive model with an average accuracy of 73%, and extract several rules. To our knowledge, this is the first attempt to use such an approach not only in the gas turbine field but also in the whole industry domain. [ABSTRACT FROM AUTHOR]

The reliability of gas turbine (GT) health state monitoring and forecasting depends on the quality of sensor measurements directly taken from the unit. Outlier detection techniques have acquired a major importance, as they are capable of removing anomalous measurements and improve data quality. To this purpose, statistical parametric methodologies are widely employed thanks to the limited knowledge of the specific unit required to perform the analysis. The backward and forward moving window (BFMW) k-σ methodology proved its effectiveness in a previous study performed by the authors, to also manage dynamic time series, i.e., during a transient. However, the estimators used by the k-σ methodology are usually characterized by low statistical robustness and resistance. This paper aims at evaluating the benefits of implementing robust statistical estimators for the BFMW framework. Three different approaches are considered in this paper. The first methodology, k-MAD, replaces mean and standard deviation (SD) of the k-σ methodology with median and mean absolute deviation (MAD), respectively. The second methodology, σ-MAD, is a novel hybrid scheme combining the k-σ and the k-MAD methodologies for the backward and the forward windows, respectively. Finally, the biweight methodology implements biweight mean and biweight SD as location and dispersion estimators. First, the parameters of these methodologies are tuned and the respective performance is compared by means of simulated data. Different scenarios are considered to evaluate statistical efficiency, robustness, and resistance. Subsequently, the performance of these methodologies is further investigated by injecting outliers in field datasets taken on selected Siemens GTs. Results prove that all the investigated methodologies are suitable for outlier identification. Advantages and drawbacks of each methodology allow the identification of different scenarios in which their application can be most effective. [ABSTRACT FROM AUTHOR]

Solar energy is considered one of the most affordable renewable resources for meeting current energy demands and mitigating environmental problems. However, the exploitation of solar energy is challenging because of both diurnal and seasonal variations. Power-to-hydrogen technologies can play a key role to counterbalance the variation of solar irradiance. Moreover, hydrogen-fueled gas turbines are considered promising technologies to decarbonize the electricity sector. To tackle these concerns, this paper presents a multigeneration energy system operated in island mode in which a hydrogen-fueled gas turbine is coupled with a solar photovoltaic plant, an electrolyzer, an absorption chiller, electric and thermal energy storage, as well as a hydrogen storage. Therefore, the energy system is 100% based on renewable energy. The sizes of the components are optimized by maximizing the exploitation of renewable energy sources, while the supply of electricity from the national grid must be null. Moreover, the effect of ambient conditions on the optimal sizing is also investigated by considering the thermal, cooling, and electrical energy demands of two case studies located in two different climatic zones. The paper demonstrates that the adoption of hydrogen-fueled gas turbines coupled with power-to-hydrogen technologies can effectively support the transition toward a clean energy supply. Moreover, this study provides a procedure for the optimal sizing of a multigeneration energy system fully based on solar energy, by also demonstrating that both photovoltaic (PV) panel area and hydrogen storage volume are feasible, if compared to the considered district layout. [ABSTRACT FROM AUTHOR]

The exploitation and utilisation of solar energy is challenging because of both diurnal and seasonal variation. Seasonal thermal energy storage is a prominent solution to solve the problem of seasonal variation of solar production. This paper investigates both the optimal design and energy management of a renewable energy plant with seasonal thermal energy storage. As a case study, the thermal, cooling and electrical energy demands of a university campus during one year are considered. Three scenarios, characterized by a different available area (20,555 m2, area covered at present; 50,000 m2, potentially available area by exploiting the entire rooftop and parking area; unlimited area) for solar thermal collectors and photovoltaic panels are investigated. In the first two scenarios, the exploitation of solar energy allows a primary energy saving of 31% and 57%, respectively compared to the use of a conventional plant. The seasonal storage reaches an overall efficiency (thus considering both charging and discharging) up to 84%. Finally, the analysis of the third scenario shows that the feasibility of meeting the energy demands by only exploiting solar energy is technically challenging because a large area equal to about 100,293 m2 is needed for solar thermal collectors and photovoltaic panels. [ABSTRACT FROM AUTHOR]

This study aimed to identify the impact of supplementing rumen-protected methionine (RPM) during the periconceptional period on beef cows in their progeny. Evidence suggests that changes in maternal nutrition in the periconceptional period have effects during the postnatal growth phase in cattle. We hypothesized that feeding RPM during the periconceptional period would program bovine gestation to enhance progeny growth. A total of 114 beef cows were fed a roughage-based diet and randomized to receive corn gluten supplemented with 15 g of RPM (Smartamine M, Adisseo) or not (CON) from d -7 to +7 relative to artificial insemination (AI) with female sex-sorted semen. Pregnancy diagnoses were conducted using transrectal ultrason-ography 30 and 60 d after AI. Female calves (n = 34) remained with their dams until weaning. A month after weaning, calves were weighed every 14 d during an 8-mo period, and wither height (WH), body length (BL), and heart girth (HG) were measured. Dry matter intake (DMI), average daily gain (ADG), and residual feed intake (RFI) were measured during a 56-d performance test. At the end of the performance test, the 12th rib back fat (BF), ribeye area (REA), intramuscular fat (IMF), and rump fat (RF) were determined via ultrasound. A treatment-by-day interaction tendency (P = 0.10) was observed in HG, having larger HG in the RPM treatment from d 336-adj of age (CON = 154.52 ± 1.46 cm; RPM = 158.36 ± 1.34 cm). During the performance test, animals in the RPM treatment were heavier (CON = 437.25 ± 2.46 kg; RPM = 442.79 ± 2.34 kg; P = 0.01) at the end of the test. However, no treatment difference (P > 0.05) was observed in DMI (CON = 12.06 ± 0.19 kg/d; RPM = 12.14 ± 0.18 kg/d), ADG (CON = 1.50 ± 0.03 kg; RPM = 1.55 ± 0.03 kg) and RFI (CON = -0.12 ± 0.15; RPM = 0.11 ± 0.14). No treatment difference (P > 0.05) was observed in BF, REA, and IMF. However, RF was greater in the control treatment (CON = 1.16 ± 0.07 cm; RPM = 0.91 ± 0.06 cm), and a tendency of having a thicker BF in control was observed (Table 1). In conclusion, supplementing RPM enhanced final body weight during the performance test; furthermore, a treatment-by-day interaction tendency was observed in HG. On the contrary, CON progeny had more RF and a tendency to have a thicker BF. [ABSTRACT FROM AUTHOR]

This paper analyses the application of two fault tolerant control schemes to a hydroelectric model developed in the Matlab and Simulink environments. The proposed fault tolerant controllers are exploited for regulating the speed of the Francis turbine included in the hydraulic system. The nonlinear behaviour of the hydraulic turbine and the inelastic water hammer effects are taken into account in order to develop a high-fidelity simulator of this dynamic plant. The first fault tolerant control solution relies on an adaptive control design, which exploits the recursive identification of a linear parametric time-varying model of the monitored system. The second scheme proposed uses the identification of a fuzzy model that is exploited for the reconstruction of the fault affecting the system under diagnosis. In this way, the fault estimation and its accommodation is possible. Note that these strategies, which are both based on identification approaches, are suggested for enhancing the application of the suggested fault tolerant control methodologies. These characteristics of the study represent key issues when on-line implementations are considered for a viable application of the proposed fault tolerant control schemes. The faults considered in this paper affect the electric servomotor used as a governor, the hydraulic turbine speed sensor, and the hydraulic turbine system, and are imposed both separately and simultaneously. Moreover, the complete drop of the rotational speed sensor is also analysed. Monte-Carlo simulations are also used for analysing the most important issues of the proposed schemes in the presence of parameter variations. Moreover, the performances achieved by means of the proposed solutions are compared to those of a standard PID controller already developed for the considered model. Finally, these strategies serve to highlight the potential application of the proposed control strategies to real hydraulic systems. [ABSTRACT FROM AUTHOR]

Highlights: [•] Analysis of the most recent technologies to exploit low-grade thermal energy. [•] Review of state-of-the-art research and development of ORC systems. [•] Comprehensive review of modeling approaches for ORC system simulation. [•] Guidelines to develop a powerful simulation tool for ORC systems. [•] Development and validation of a micro-ORC system simulation model. [ABSTRACT FROM AUTHOR]

This paper presents the results of the application of an advanced thermodynamic model developed by the authors for the simulation of Organic Rankine Cycles (ORCs). The model allows ORC simulation both for steady and transient analysis. The expander, selected to be a scroll expander, is modeled in detail by decomposing the behavior of the fluid stream into several steps. The energy source is coupled with the system through a plate heat exchanger (PHE), which is modeled using an iterative sub-heat exchanger modeling approach. The considered ORC system uses solar thermal energy for ultralow grade thermal energy recovery. The simulation model is used to investigate the influence of ORC characteristic parameters related to the working medium, hot reservoir and component efficiencies for the purpose of optimizing the ORC system efficiency and power output. Moreover, dynamic response of the ORC is also evaluated for two scenarios, i.e. (i) supplying electricity for a typical residential user and (ii) being driven by a hot reservoir. Finally, the simulation model is used to evaluate ORC capability to meet electric, thermal and cooling loads of a single residential building, for typical temperatures of the hot water exiting from a solar collector. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an analysis aimed to evaluate the feasibility of micro-CHP systems to meet the household energy demands of single family users. The considered CHP systems, with an electric power output of a few kW, are based on technologies (internal combustion engines, micro gas turbines, micro Rankine cycles, Stirling engines and thermophotovoltaic generators) which are already available on the market or will be available in the near future. Each CHP system is composed of (i) a prime mover, (ii) a thermal energy storage unit and (iii) an auxiliary boiler used to cover peak thermal demands. The analyses performed in this paper aim to evaluate (i) the energy performance of the CHP systems to meet the energy demand of two single-family dwellings, with different floor areas and shape factors, and (ii) the maximum cost allowed for each CHP system. The results presented in this paper allow the most suitable technology, prime mover and thermal energy storage unit size to be identified. From an energy point of view, the considered CHP systems usually satisfy most of the thermal and electric energy demand, with a primary energy saving index that is always higher than 20%. Moreover, the correct sizing of the thermal energy storage unit capacity proves crucial. As regards economic feasibility, it is shown that a reasonable target for the marginal cost of a CHP system for household heating is approximately 3000€/kWe, even though the absence of incentives does not make any of the technologies very attractive at present. It is also shown that the highest profitability can be obtained by using the prime mover (i) with the lowest electric power output closest to that of the peak electric demand and (ii) with an electric-to-thermal ratio which fits the electric-to-thermal ratio of the users. [Copyright &y& Elsevier]

A reduction of gas turbine maintenance costs, together with the increase in machine availability and the reduction of management costs, is usually expected when gas turbine preventive maintenance is performed in parallel to on-condition maintenance. However, on-condition maintenance requires up-to-date knowledge of the machine health state. The gas turbine health state can be determined by means of Gas Path Analysis (GPA) techniques, which allow the calculation of machine health state indices, starting from measurements taken on the machine. Since the GPA technique makes use of field measurements, the reliability of the diagnostic process also depends on measurement reliability. In this paper, a comprehensive approach for both the measurement validation and health state determination of gas turbines is discussed, and its application to a 5MW gas turbine working in a natural gas compression plant is presented. [ABSTRACT FROM AUTHOR]

Abstract: The use of biogas for energy production has progressively increased in recent years, due to an increasing interest both in agricultural and energy policies of many industrialized countries. Biogas compression by means of natural gas infrastructure seems the most immediate solution, but could also lead to problems due to the different physical properties of the two gases. In this paper, a non-linear one-dimensional modular dynamic model is developed and used for the simulation of compression system transient behavior. The arrangement consists of a main line, where the compressor operates, and an anti-surge control, which consists of a recycle loop activated by a fast acting valve. Different maneuvers (start-up, normal operation, emergency shutdown and operating point variation) are simulated by using two different working fluids (methane and biogas). Simulations prove that the design of the surge protection system should consider the fluid to be elaborated. Moreover, system predisposition to surge increases as the ratio between system volumes and the inertia of the rotating masses increases. [Copyright &y& Elsevier]

This paper presents a physics-based approach aimed to predict the performance curves of PATs (pumps as turbines). The methodology includes a tuning procedure, which allows the calibration of the physics-based model on a given dataset of pumps/PATs, and a prediction procedure, which allows the estimation of PAT performance curves for previously unknown PATs. The methodology is applied to six pumps/PATs, of which the performance curves were experimentally determined at different rotational speeds. A cross-validation process is applied in such manner that one of the six pumps/PATs is not employed for the tuning procedure and is used for validating the reliability of the prediction procedure. The results show that, in the range ±20% with respect to the experimental BEP, the deviation of the predicted PAT performance curves from experimental curves can be acceptable from an engineering point of view, with an average value of 13.4%, 14.0% and 12.4%, for head, power and efficiency, respectively. • Physics-based comprehensive methodology for predicting PAT performance curves. • Investigation of twenty-three experimental datasets for both pumps and PATs. • Tuning procedure to calibrate the physics-based model parameters. • Prediction procedure to forecast PAT performance curves. • Producible energy by means of the predicted PAT performance curves. [ABSTRACT FROM AUTHOR]

Abstract With their fast growth rate and ability to accumulate a high percentage of their weight as lipid and carbohydrate, microalgae potentially represent an ideal feedstock for the production of biodiesel and bioethanol. In addition, microalgae offer several environmental benefits, and do not compete with food production for land, fresh water, and nutrients. Therefore, the main goal of this work is to provide a quantitative, systematic and harmonized assessment of current bio-energy potential. The analysis is conducted by considering all the main steps in detail, from cultivation to biodiesel production, and by deriving an overall estimation of energy consumption for biodiesel production. Energy consumption uncertainty is also quantified and discussed. A systematic review of all the main technologies available for all the main processing steps towards the production of biodiesel from microalgae is presented, focusing on the derivation of the Net Energy Ratio (NER) of each combination of technologies, complemented by an uncertainty analysis of the data used and those obtained in the present work. A wide scatter in the data available in the literature has been identified, highlighting the need for an uncertainty analysis. If the average overall energy consumption per unit of biodiesel mass is considered, all the routes adopting a raceway pond have a lower energy consumption, but if the uncertainty on the overall energy consumption is also considered, the minimum value of the range of NER values for some of the routes adopting a photobioreactor is comparable to the NER value obtainable by using raceway ponds. Thus, the present framework proposes a harmonized and comprehensive methodology to compare and contrast technologies for the production of biodiesel from microalgae, and is applied in this paper to identify, with an appreciation of the uncertainty, the most promising combinations of technologies. Highlights • Quantitative, systematic and harmonized assessment of microalgae energy potential. • A step-by-step analysis and overall estimation of energy consumption is given. • A substantial uncertainty on the data in literature has been identified. • The energy consumption uncertainty is quantified and critically discussed. • Derivation of the Net Energy Ratio (NER) for each combination of technologies. [ABSTRACT FROM AUTHOR]

• Diagnostic approach to evaluate the health state of District heating Networks (DHNs). • Detection and identification of leakages, anomalous heat and pressure losses. • Detection and identification of single and multiple faults. • Validation of the diagnostic approach that correctly detects and identifies all implanted faults. District Heating Networks (DHNs) are composed of numerous pipes that can be threatened by faults that affect DHN operation and management. Thus, reliable diagnostic methodologies are essential to identify DHN health state and hinder DHN malfunctioning and performance deterioration. To this purpose, a novel diagnostic approach that couples a DHN simulation model with an optimization algorithm for detecting and identifying both thermal and hydraulic faults, i.e., water leakages, anomalous heat and pressure losses, is presented in this paper. In the current paper, the novel diagnostic approach is challenged at evaluating the health state of the DHN of the campus of the University of Parma, where different faults are artificially implanted, by using a digital twin of the DHN. The faulty datasets account for both single and multiple faults, as well as different fault types and causes. The novel diagnostic approach proves to correctly detect and identify all simulated faults, by also correctly estimating their magnitude even in the most challenging scenarios. [ABSTRACT FROM AUTHOR]

District Heating Network (DHN) pipes can be affected by faults that endanger system reliability. Thus, this paper develops a novel modeling and diagnostic approach for the detection and identification of the most frequent faults that affect DHN pipes, i.e., water leakages, heat losses and pressure losses. The modeling approach exploits physics-based equations for calculating all DHN measurable variables, i.e., flow rate, temperature and pressure. The diagnostic approach detects and identifies pipe faults by coupling the modeling approach with an optimization algorithm. As a result, the diagnostic approach provides the health indices of each pipe of the DHN, which identify the faulty pipe, the fault type and its magnitude. The modeling approach proves to be extremely accurate since the Root Mean Square Error of the DHN variables is always lower than 0.02%. Furthermore, the modeling approach is exploited to infer general guidelines about the DHN health state to investigate the relationship between health indices and DHN characteristics. The novel diagnostic approach is verified by implanting six faults in the DHN of the campus of the University of Parma. All faults are correctly detected and identified, by also evaluating the exact fault magnitude. • Detection and identification of faults in District Heating Networks (DHNs). • Modeling and diagnostic approaches for DHNs. • Guidelines for detecting faults in DHNs. • Validation of the diagnostic approach by means of implanted faults. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to estimate the biomass energy potential and its associated uncertainty at a country level when quality and availability of data are limited. The current biomass energy potential in Colombia is assessed following the proposed methodology and results are compared to existing assessment studies. The proposed methodology is a bottom-up resource-focused approach with statistical analysis that uses a Monte Carlo algorithm to stochastically estimate the theoretical and the technical biomass energy potential. The paper also includes a proposed approach to quantify uncertainty combining a probabilistic propagation of uncertainty, a sensitivity analysis and a set of disaggregated sub-models to estimate reliability of predictions and reduce the associated uncertainty. Results predict a theoretical energy potential of 0.744 EJ and a technical potential of 0.059 EJ in 2010, which might account for 1.2% of the annual primary energy production (4.93 EJ). [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a novel method to estimate the future biomass energy potential in countries with domestic markets unable to influence international markets. As a study case, the biomass energy potential in Colombia is estimated for the period 2010–2030. The prediction model is a scenario-based optimization algorithm that maximizes the yearly profit of locally producing and importing commodities in a country subject to certain constraints (domestic demand, limited area, etc.) as well as to demographic, macroeconomic and market data (e.g. domestic and international prices of commodities). The bioenergy potential associated to the production of commodities is calculated according to a methodology presented by the same authors. In order to provide a modeling framework consistent with other state-of-the-art projections, global scenarios for analysis are selected from the literature rather than formulated. Selected global scenarios highlight the influence of global biofuel use on agricultural prices, production and demand. Results predict a theoretical bioenergy potential in Colombia 56%–69% larger in 2030 than in 2010 (1.31–1.41 EJ). A sensitivity analysis shows that while a higher global biofuel use leads to a higher local bioenergy potential, its influence is less pronounced than that of agricultural yields, demand and specific energy of biomass resources. [Copyright &y& Elsevier]

Highlights: [•] Minimization by means of a genetic algorithm of the annual flux of primary energy. [•] CHP can represent an option only for efficiencies higher than a threshold value. [•] The threshold value depends on integrated energy system characteristics. [ABSTRACT FROM AUTHOR]

• Development of a general methodology for the design of a distributed energy plant. • Life cycle assessment is accounted for in the design stage. • Scaling curves for the quantification of energy and environmental impacts. • The fossil cumulative energy demand over the life cycle can be reduced up to 31%. • The total investment and operational costs can be reduced up to 12%. Distributed energy plants can be relevant to mitigate global energy consumption and carbon emissions. The energy and environmental advantages of these systems can be achieved through an optimal design by considering their life cycle energy consumption and environmental impacts. However, the high technical complexity and the scarcity of life cycle inventory data about distributed energy plants makes their design optimization very challenging. This paper proposes a design optimization methodology for distributed energy plants by accounting for life cycle impacts. The plant comprises renewable energy systems, fossil fuel energy systems and energy storage technologies. The life cycle inventory data are scaled at different sizes by using scaling laws which are obtained by gathering data of commercially available systems of various sizes and from different manufacturers. In this manner, impact scaling curves for the quantification of energy and environmental impacts like fossil cumulative energy demand, global warming potential and abiotic resource depletion are obtained. The validity of the proposed methodology is demonstrated by considering the campus of the University of Parma (Italy) as a case study. A distributed energy plant is optimally designed by using a mixed-integer genetic algorithm to minimize life cycle fossil cumulative energy demand. Moreover, an economic assessment of the optimal configurations is also performed. Compared to a conventional plant, the configuration with combined heat and power systems allows a primary energy saving of about 15% and a reduction of total costs of about 12%, while the configuration with reversible heat pumps is the most expensive. [ABSTRACT FROM AUTHOR]

• Methodology for the simultaneous optimization of the design and operation of MESs. • Multi-objective optimization based on life cycle energy and economic assessment. • Life cycle energy saving of 17%, compared to a conventional plant. • Total cost reduction of about 18%, compared to a conventional plant. • The proposed methodology significantly reduces the computational time. Multi-generation energy systems could mitigate global energy consumption, carbon emissions and economic costs. The promising energy, environmental and economic advantages of these systems are greatly dependent on their design and operational strategy. Consequently, methods and guidelines for the optimal design and operation of such systems, also taking into consideration their life cycle, are needed to fully exploit their potential. This paper proposes a general methodology for the simultaneous optimization of the design and operation of multi-generation energy systems by considering life cycle energy and economic assessment. The design optimization problem is solved by means of surrogate modeling and the operation optimization problem by means of dynamic programming. The multi-generation energy system considered in this paper comprises renewable energy systems, fossil fuel energy systems and energy storage technologies. Different multi-objective optimizations were performed by considering the minimization of fossil cumulative energy demand, and total investment and operational costs. The validity of the proposed methodology is demonstrated by using the campus of the University of Parma (Italy) as a case study. Compared to a conventional plant, the optimal solution allows a life cycle energy saving of about 17% and total cost reduction of about 18%. Moreover, compared to an optimization method based on particle swarm optimization and dynamic programming, the proposed methodology provides comparable results, but the computation time is 78% lower. The proposed methodology outperforms commonly used optimization algorithms and provides an effective and flexible framework for the optimal design and operation of multi-generation energy systems. [ABSTRACT FROM AUTHOR]

Three-dimensional numerical simulations of the effect of fouling on an axial compressor stage were carried out. As a case study, the NASA Stage 37 was considered for the numerical investigation, which was performed by means of a commercial computational fluid dynamic code. The numerical model was validated against the experimental data available from literature. Computed performance maps and main flow field features showed a good agreement with the experimental data. The model was considered representative of a realistic compressor stage: The model was then used to simulate the occurrence of fouling by imposing different combinations of added thickness and surface roughness levels. The effect offouling on compressor performances was studied. Reductions in the flow coefficient and in the pressure coefficient were found to be of the same order of magnitude of the experimental results found in literature. The model developed seems to overcome some of the limitations of other models found in literature that tend to significantly underestimate the actual values of performance reduction. The numerical results were also used to analyze and debug the stage performance scaling procedure used in stage-stacking models in order to represent fouling in multistage compressors. The analysis highlighted that scaling can adequately represent the behavior of the fouled stage in the choked flow region, but it does not capture the reduction in the minimum of the pressure coefficient, which is instead revealed by the numerical simulations. Finally, blockage due to fouling was investigated both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

Offshore wind turbines are now a mature technology to produce renewable energy on a vast scale, nonetheless several design and maintenance planning challenges remain. There have been attempts to investigate the impact of marine growth on fixed offshore wind turbine structures, but only few adopted a whole dynamics approach. This work presents a methodology to capture the influence of marine growth on the dynamic response of a tripod substructure, supporting the NREL 5 MW reference offshore wind turbine, under combined dynamic loads from wind and waves, and including soil-structure interaction by means of the spring-to-ground model. Marine growth is modelled as prescribed by DNV and API, evaluating the effects of variation of its thickness, roughness, and distribution. It is here demonstrated that marine growth thickness and roughness impact significantly on the loads acting on wind turbines' structures and its dynamic response, and that heterogeneity in marine growth thickness profiles vs depth available in literature lead to substantially different results. Tower top displacement becomes 24% higher when marine growth thickness grows from 0 to 200 mm. On the other hand, the changes in the natural frequencies of the support structure with an increase of marine growth's thickness are almost negligible (0.3%). • Analysis of the effect of marine growth properties for different sea states on offshore wind turbine support structures. • As marine growth layers thicken, load values on the support structure increase continuously in every scenario. • Great sensitivity to roughness modelling, suggesting the importance of this parameter. • No appreciable variation of the natural frequencies, a critical parameter for resonance. • Guidelines given from different codes for marine growth distribution were compared. [ABSTRACT FROM AUTHOR]

In recent years, residential users have begun to be equipped with micro-CHP (combined heat and power) generation technologies with the aim of decreasing primary energy consumption and reducing environmental impact. In these systems, the prime mover supplies both thermal and electrical energy, and an auxiliary boiler and the national electrical grid are employed as supplementary systems. In this paper, a simulation model, which accounts for component efficiency and energy balance, was developed to replicate the interaction between the users and the energy systems in order to minimize primary energy consumption. The simulation model identified the optimal operation strategy of two residential users by investigating different energy system configurations by means of a dynamic programming algorithm. The reference scenario was compared to three different scenarios by considering independent energy systems, shared thermal and electrical energy storage and also the shared prime mover. Such a comparison allowed the identification of the most suitable energy system configuration and optimized operation strategy. The results demonstrate that the optimized operation strategy smoothes the influence of the size of thermal and electrical energy storage. Moreover, the saving of primary energy consumption can be as high as 5.1%. The analysis of the economic feasibility reveals that the investment cost of the prime mover can be as high as 4000 €/kW. [ABSTRACT FROM AUTHOR]

• Sizing and operation optimization of a hybrid energy plant. • Integration of life cycle assessment into optimization. • Non-linear life cycle inventory scaling of energy systems. • Estimation of the operational primary energy and cumulative energy demand. This paper deals with the optimal design of a hybrid energy plant, which can include the following energy systems: solar thermal collector, photovoltaic panel, hybrid photovoltaic/thermal solar system, combined heat and power system, organic Rankine cycle, absorption chiller, air source heat pump, ground source heat pump and thermal energy storage. Three different configurations are analyzed. In the first configuration, the abovementioned systems are considered with the exception of the hybrid photovoltaic/thermal solar system and organic Rankine cycle. In the second configuration, a hybrid photovoltaic/thermal solar system is also included and in the third configuration the use of an organic Rankine cycle as the bottoming cycle of the combined heat and power system is evaluated. The optimization goal is to minimize the primary energy demanded throughout the manufacturing and operation phase of the hybrid energy plant. The challenge of non-linear life cycle inventory scaling of energy systems is also addressed. A tower located in northern Italy is selected as a case study and two different approaches are evaluated. The first approach consists of solving the sizing optimization by minimizing primary energy consumption only during the operation phase, while in the second approach primary energy consumption is minimized throughout the life cycle of the plant by integrating the life cycle assessment into the optimization process. The results show that, if life cycle assessment is integrated, the optimal sizes of plant components are different and the primary energy saving throughout the life cycle is always higher. With reference to the LCA integrated approach and compared to the first configuration, the use of a hybrid photovoltaic/thermal solar system instead of separate solar thermal collector and photovoltaic panels is more efficient and may allow a primary energy saving of about 4%. Furthermore, compared to a conventional plant, the primary energy saving achievable with the first configuration is approximately 14%, while the primary energy saving increases to about 17% for the second and third configurations. [ABSTRACT FROM AUTHOR]