Vibration analysis of the civic tower in Rieti

In the last decades the definition of a suitable monitoring system for identifying the dynamic behavior of structures has had a central position in the civil engineering research area. The vibration analysis leads to the recognition of the reference state of structures which is essential to determine the integrity level when extreme events occur, such as earthquakes. The latest seismic events occurred in the world have shown the essential role of the new passive seismic techniques which aim to protect structures and the importance of supervising the building construction operations and the adopted improvement measures. In this work the structural monitoring of the civic tower located in Rieti is presented. In the tower a non-conventional TMD has been installed via an inter-story isolation system at the top floor by means of High Damping Rubber Bearings (HDRB). The general goal is to define a monitoring system suitable with this experimental case through the vibration analysis. Several aspects will be taken into account: the choice of sensors setup, the measured quantities and the extraction of structural information. Firstly this will allow to define the structure’s reference state featured by frequencies, damping ratios and mode shapes. Moreover the effective design of the monitoring system would lead to the characterization of the dynamic behavior of the structure equipped with a passive vibration control system. Different tests have been carried forward: ambient vibration test (AVT), forced vibration test (FVT) with vibrodyne and seismic test (ST). The AVT and the FVT enable to define the monitoring system and check the reliability of the adopted identification tools, among which an Output Only algorithm stands out: the Observer Kalman Filter System Id. On the other hand the ST will point out some preliminary information about the dynamic behaviour of the structure equipped with a non conventional Tuned Mass Damper referring it to higher levels of vibrations

A Lagrangian kinetic model for collisionless magnetic reconnection

A new fully kinetic system is proposed for modeling collisionless magnetic reconnection. The formulation relies on fundamental principles in Lagrangian dynamics, in which the inertia of the electron mean flow is neglected in the expression of the Lagrangian, rather then enforcing a zero electron mass in the equations of motion. This is done upon splitting the electron velocity into its mean and fluctuating parts, so that the latter naturally produce the corresponding pressure tensor. The model exhibits a new Coriolis force term, which emerges from a change of frame in the electron dynamics. Then, if the electron heat flux is neglected, the strong electron magnetization limit yields a hybrid model, in which the electron pressure tensor is frozen into the electron mean velocity.Comment: 15 pages, no figures. To Appear in Plasma Phys. Control. Fusio

A geometric observer-assisted approach to tailor state estimation in a bioreactor for ethanol production

In this work, a systematic approach based on the geometric observer is proposed to design a model-based soft sensor, which allows the estimation of quality indexes in a bioreactor. The study is focused on the structure design problem where the set of innovated states has to be chosen. On the basis of robust exponential estimability arguments, it is found that it is possible to distinguish all the unmeasured states if temperature and dissolved oxygen concentration measurements are combined with substrate concentrations. The proposed estimator structure is then validated through numerical simulation considering two different measurement processor algorithms: the geometric observer and the extended Kalman filter

Different control strategies for a yeast fermentation bioreactor

Biological systems are usually highly sensitive to process conditions variations, such as temperature, pH, substrate concentration. For this reason, it is important to adequately control and monitor the process in order to guaranteeing product quality while maintaining adequate performance and productivity. The production of ethanol by fermentation is certainly one of the most important industrial bioprocesses, being ethanol an alternative source of energy. For this reason, valuable models of this process based on different kinetic considerations are available in literature, and they can be considered a valid benchmark to investigate control system and estimation techniques for biological reactors. Three different control strategies have been analysed: direct reactor temperature control, cascade control where the primary loop uses delayed ethanol measurements, and 2x2 control system with inferential control for the product concentration. The proposed configurations have been compared at different operating conditions and results show that the use of the inferential control is the most effective in case of severe disturbances

Damage detection in a RC-masonry tower equipped with a non-conventional TMD using temperature-independent damage sensitive features

Many features used in Structural Health Monitoring strategies are not just highly sensitive to failure mechanisms, but also depend on environmental or operational fluctuations. To prevent incorrect failure uncovering due to these dependencies, damage detection approaches can use robust and temperature-independent features. These indicators can be naturally insensitive to environmental dependencies or artificially made independent. This work explores both options. Cointegration theory is used to remove environmental dependencies from dynamic features to create highly sensitive parameters to detect failure mechanisms: the cointegration residuals. This paper applies the cointegration technique for damage detection of a concrete-masonry tower in Italy. Two regression models are implemented to capture temperature effects: Prophet and Long Short-Term Memory networks. Results demonstrate the advantages and limitations of this methodology for real applications. The authors suggest to combine the cointegration residuals with a secondary temperature-insensitive damage-sensitive set of features, the Cepstral Coefficients, to address the possibility of capturing undetected structural damage

Modeling a biological reactor using sparse identification method

In this work a model-based controller for a fermentation bioreactor has been developed. By simulating the model of the process that acts as a virtual plant, input-output data have been generated and used to identify the system using sparse identification of nonlinear dynamics methodology. The obtained model is then used in a model-based algorithm to control the bioreactor temperature, where the manipulated action is obtained as a result of a constrained nonlinear optimization problem which minimizes the mismatch between the predicted trajectory and the desired one. Good performances have been obtained by applying the proposed control strategy for set-point changes and disturbance rejection

Adaptive feedback control for a pasteurization process

The milk pasteurization process is nonlinear in nature, and for this reason, the application of linear control algorithms does not guarantee the obtainment of the required performance in every condition. The problem is here addressed by proposing an adaptive algorithm, which was obtained by starting from an observer-based control approach. The main result is the obtainment of a simple PI-like controller structure, where the control parameters depend on the state of the system and are adapted online. The proposed algorithm was designed and applied on a simulated process, where the temperature dependence of the milk's physical properties was considered. The control strategy was tested by simulating different situations, particularly when time-varying disturbances entered the system. The use of the adaptive rule reduces the variance generally introduced by the PI or PID controller

Dynamic simulator and model predictive control of a milk pasteurizer

In this study, the design, optimization and dynamic modelling of a milk pasteurization unit have been developed, using the pseudo-component approach for describing milk properties. The fluid has been regarded as a mixture of five major categories, namely water, fats, proteins, carbohydrates, and minerals. Exploiting the optimal pasteurizer configuration, selected based on the total annualized cost, a dynamic model of the process has been also derived. The simulation of the system is then used as a virtual plant to develop a nonlinear model predictive control (NMPC) designed for rejecting the more important disturbances that can enter the system. The predicted trajectories have been calculated with a simplified version of the dynamic model, obtained by neglecting parameters temperature dependence. The NMPC performance has been compared with a PI controller in terms of set-point tracking and disturbance rejection. Similar results have been obtained when using the different control algorithms for the output responses, but the NMPC showed better behaviour of the manipulated variables

EOQ inventory model for perishable products under uncertainty

Perishable products require accurate inventory control models as their effect on operations management can be critical. This assumption is particularly relevant in highly uncertain and dynamic markets, as for the ones generated by the pandemic era. This paper presents an inventory control model for perishable items with a demand rate variable over time, and dependent on the inventory rate. The model also considers the potential for backlogging and lost sales. Imperfect product quality is included, and deterioration is modelled as a time-dependent variable. The framework envisages the possibility to define variables affected by uncertainty in terms of probability distribution functions, which are then jointly managed via a Monte Carlo simulation. This paper is intended to provide an analytical formulation to deal with uncertainty and time-dependent inventory functions to be used for a variety of perishable products. The formulation is designed to support decision-making for the identification of the optimal order quantity. A numerical example exemplifies the outcomes of the paper and provides a cost-based sensitivity analysis to understand the role of main parameters

A system-approach for recoverable spare parts management using the discreteweibull distribution

Optimal spare parts management strategies allow sustaining a system's availability, while ensuring timely and effective maintenance. Following a systemic perspective, this paper starts from the Multi-Echelon Technique for Recoverable Item Control (METRIC) to investigate the potential use of a Weibull distribution for modelling items' demand in case of failure. Adapting the analytic formulation of METRIC through a Discrete Weibull distribution, this study originally proposes a METRIC-based model (DW-METRIC) to be used for modelling the stochastic demand in multi-item systems, in order to ensure process sustainability. The DW-METRIC has been tested in a case study related to an industrial plant constituted by 98 items in a passive redundancy configuration. Comparing the results via a simulation model, the outcomes of the study allow defining applicability criteria for the DW-METRIC, in those settings where the DW-METRIC offers more accurate estimations than the traditional METRIC