A compare review about equilibrium conditions of semi-clathrate hydrate: experimental measurements visions and thermodynamic modeling aspects

The formation of gaseous (gas) hydrates for storage, separation and transportation application is essential. In this regard, a comprehensive study of this case is essential. Semi-clathrate hydrates have higher temperature stability and are formed in a stable range. The purpose of this study is review the experimental and modeling of the semi-clathrate hydrates, to investigate the equilibrium conditions for the formation/dissociation of them based on the type of thermodynamic promoters like TBAB, TBAC, TBAF, TBANO₃ and TBP groups. This review is consist of 4 overall section, at first an introduction to semi-clathrate hydrates has defined. After that, the experimental research has discussed through different gas systems such as CO₂, CH₄, N₂, H₂ etc. Also, the target of each study, like the CO₂ capture, separation of CH4, formation/dissociation equilibrium conditions, are expressed. Then, in the modeling section, the different types of thermodynamic modeling like, equaling fugacity, intelligence computing, Gibbs free energy minimization and Chen-Guo method are collected. At final section, a comparison between types of promoter showed that the addition of TBAF to aqueous solution has the best promotion effect on the CO₂ clathrate hydrate formation. Also, the results of comparing the concentration of promoters have shown that up to a certain amount of TBAB, the salt's role as a promoter and by addition concentration of promoter, has an inhibition effect. Also, besides the results of the comparison different promoters on equilibrium conditions of different gaseous hydrates, have indicated that, TBAB has the most significant impact on carbon dioxide hydrate.

     

Design a robust sliding mode controller based on the state and parameter estimation for the nonlinear epidemiological model of Covid-19

In this research, the challenging problem of Covid-19 mitigation is looked at from an engineering point of view. At first, the behavior of coronavirus in the Iranian and Russian societies is expressed by a set of ordinary differential equations. In the proposed model, the control input signals are vaccination, social distance and facial masks, and medical treatment. The unknown parameters of the system are estimated by long short-term memory (LSTM) algorithm. In the LSTM algorithm, the problem of long-term dependency is prevented. The uncertainty and measurement noises are inherent characteristics of epidemiological models. For this reason, an extended Kalman filter (EKF) is developed to estimate the state variables of the proposed model. In continuation, a robust sliding mode controller is designed to control the spread of coronavirus under vaccination, social distance and facial masks, and medical treatment. The stability of the closed-loop system is guaranteed by the Lyapunov theorems. The official confirmed data provided by the Iranian and Russian ministries of health are employed to simulate the proposed algorithms. It is understood from simulation results that global vaccination has the potential to create herd immunity in long term. Under the proposed controller, daily Covid-19 infections and deaths become less than 500 and 10 people, respectively.