Analisi della produzione e consumo della carne di coniglio: un modello di equilibrio parziale

Within the Italian animal breeding sector, rabbit is one of the most important species and it is of great interest also at international level. The rabbit breeding sector is characterized by high variability of market prices which fluctuate in the short term as well as in the medium and long term. This trend is determined by technical factors but also by the business climate and structural problems. The fluctuation of market prices causes problems for the breeders in combining the supply with the demand. Therefore, the breeders are interested in having knowledge of the relationship of price and quantity produced, in order to plan their production. The partial equilibrium model identifies the above mentioned relationship and determine the quantity of meat that should be supplied on the market so that the producer’s price equals the cost of production

Artificial Intelligence for the Diagnostics of Gas Turbines. Part I: Neural Network Approach

In the paper, Neural Network (NN) models for gas turbine diagnostics are studied and developed. The analyses carried out are aimed at the selection of the most appropriate NN structure for gas turbine diagnostics, in terms of computational time of the NN training phase, accuracy and robustness with respect to measurement uncertainty. In particular, feed-forward NNs with a single hidden layer trained by using a back-propagation learning algorithm are considered and tested. Moreover, Multi-Input/Multi-Output NN architectures (i.e. NNs calculating all the system outputs) are compared to Multi-Input/Single-Output NNs, each of them calculating a single output of the system. The results obtained show that NNs are robust with respect to measurement uncertainty, if a sufficient number of training patterns are used. Moreover, Multi-Input/Multi-Output NNs trained with data corrupted with measurement errors seem to be the best compromise between the computational time required for NN training phase and the NN accuracy in performing gas turbine diagnostics

Artificial intelligence for the diagnostics of gas turbines. Part II: Neuro-Fuzzy approach

In the paper, Neuro-Fuzzy Systems (NFSs) for gas turbine diagnostics are studied and developed. The same procedure used previously for the set up of Neural Network (NN) models was used. In particular, the same database of patterns was used for both training and testing the NFSs. This database was obtained by running a Cycle Program, calibrated on a 255 MW single shaft gas turbine working in the ENEL combined cycle power plant of La Spezia (Italy). The database contains the variations of the Health Indices (which are the characteristic parameters that are indices of gas turbine health state, such as efficiencies and characteristic flow passage areas of compressor and turbine) and the corresponding variations of the measured quantities with respect to the values in new and clean conditions. The analyses carried out are aimed at the selection of the most appropriate NFS structure for gas turbine diagnostics, in terms of computational time of the NFS training phase, accuracy and robustness towards measurement uncertainty during simulations. In particular, Adaptive Neuro-Fuzzy Inference System (ANFIS) architectures were considered and tested, and their performance was compared to that obtainable by using the NN models. An analysis was also performed in order to identify the most significant ANFIS inputs. The results obtained show that ANFISs are robust with respect to measurement uncertainty, and, in all the cases analyzed, the performance (in terms of accuracy during simulations and time spent for the training phase) proved to be better than that obtainable by MIMO and MISO Neural Networks trained and tested on the same data

A System for Health State Determination of Natural Gas Compression Gas Turbines

This paper illustrates the policy and objectives in compression system maintenance and describes a system for the health state determination of natural gas compression gas turbines based on "Gas Path Analysis". Some results of the application of the diagnostic system to gas turbines working in a natural gas compression plant are presented

Set up of a robust neural network for gas turbine simulation

In this paper, Neural Network (NN) models for the real-time simulation of gas turbines are studied and developed. The analyses carried out are aimed at the selection of the most appropriate NN structure for gas turbine simulation, in terms of both computational time of the NN training phase and accuracy and robustness with respect to measurement uncertainty. In particular, feed-forward NNs, with a single hidden layer and different numbers of neurons, trained by using a back-propagation learning algorithm are considered and tested. Finally, a general procedure for the validation of computational codes is adapted and applied to the validation of the developed NN models