Experimental Study Of Surfactants’ Performance For Suppressing Coal Dust With Respirable Size

Long-term exposure to coal dust can lead to severe health problems in coal workers, including Coal Workers\u27 Pneumoconiosis, making effective control of coal dust in underground mines essential. Water spraying is a widely used method for controlling coal dust, and adding surfactants can remarkably enhance its effectiveness. While previous studies have examined the influences of different coal particle sizes on surfactant performance, they have primarily focused on inhalable dust with sizes less than 100 µm. The impact of finer particle sizes, such as respirable dust with sizes less than 10 µm, remains inconclusive. This study aims to investigate the effects of respirable dust ranging from 0.1 µm to 10 µm in diameter, on the performance of surfactants. It was found that the surfactants\u27 performance was weakened significantly with a decrease in the coal dust size. The suppression efficiency for coal dust size between 0.1 µm and 1.0 µm was only half that of size between 4 µm and 10 µm. The primary factors contributing to this result would be the roughness, the specific surface area, the air absorbability, and the number of particles. Furthermore, TX100 surfactant performed slightly better than SDBS in suppressing coal dust. While SDBS performed greater at a concentration of 0.15–0.20%, TX100 had higher suppression efficiency at lower concentrations. This study suggests that future research should focus on improving the suppressing performance of coal dust with finer sizes less than 0.1 µm or 2.5 µm

Design of exponential state estimators for neural networks with mixed time delays

This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2007 Elsevier Ltd.In this Letter, the state estimation problem is dealt with for a class of recurrent neural networks (RNNs) with mixed discrete and distributed delays. The activation functions are assumed to be neither monotonic, nor differentiable, nor bounded. We aim at designing a state estimator to estimate the neuron states, through available output measurements, such that the dynamics of the estimation error is globally exponentially stable in the presence of mixed time delays. By using the Laypunov–Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions to guarantee the existence of the state estimators. We show that both the existence conditions and the explicit expression of the desired estimator can be characterized in terms of the solution to an LMI. A simulation example is exploited to show the usefulness of the derived LMI-based stability conditions.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Nuffield Foundation of the UK under Grant NAL/00630/G, the Alexander von Humboldt Foundation of Germany, the Natural Science Foundation of Jiangsu Education Committee of China under Grants 05KJB110154 and BK2006064, and the National Natural Science Foundation of China under Grants 10471119 and 10671172

Distributed Robust Partial State Consensus Control for Chain Interconnected Delay Systems

Partial state consensus (PSC) is investigated for chain interconnected systems with time-varying delays and parameter uncertainties. A novel design philosophy of PSC control is proposed and a sequential calculation method is presented to guarantee the robustness of the controller. A sufficient condition based on linear matrix inequalities (LMIs) is derived and the stability is proven by the Lyapunov method. The proposed approach can ensure that the states which are subject to a consensus constraint achieve consensus, while those without a consensus constraint track their own set points. Finally, numerical simulations and a solution proportioning experiment are developed to validate the effectiveness of the proposed method

Variance-constrained dissipative observer-based control for a class of nonlinear stochastic systems with degraded measurements

The official published version of the article can be obtained from the link below.This paper is concerned with the variance-constrained dissipative control problem for a class of stochastic nonlinear systems with multiple degraded measurements, where the degraded probability for each sensor is governed by an individual random variable satisfying a certain probabilistic distribution over a given interval. The purpose of the problem is to design an observer-based controller such that, for all possible degraded measurements, the closed-loop system is exponentially mean-square stable and strictly dissipative, while the individual steady-state variance is not more than the pre-specified upper bound constraints. A general framework is established so that the required exponential mean-square stability, dissipativity as well as the variance constraints can be easily enforced. A sufficient condition is given for the solvability of the addressed multiobjective control problem, and the desired observer and controller gains are characterized in terms of the solution to a convex optimization problem that can be easily solved by using the semi-definite programming method. Finally, a numerical example is presented to show the effectiveness and applicability of the proposed algorithm.This work was supported in part by the Distinguished Visiting Fellowship of the Royal Academy of Engineering of the UK, the Royal Society of the UK, the GRF HKU 7137/09E, the National Natural Science Foundation of China under Grant 61028008, the International Science and Technology Cooperation Project of China under Grant 2009DFA32050, and the Alexander von Humboldt Foundation of Germany

State estimation for discrete-time Markovian jumping neural networks with mixed mode-dependent delays

This is the post print version of the article. The official published version can be obtained from the link - Copyright 2008 Elsevier LtdIn this Letter, we investigate the state estimation problem for a new class of discrete-time neural networks with Markovian jumping parameters as well as mode-dependent mixed time-delays. The parameters of the discrete-time neural networks are subject to the switching from one mode to another at different times according to a Markov chain, and the mixed time-delays consist of both discrete and distributed delays that are dependent on the Markovian jumping mode. New techniques are developed to deal with the mixed time-delays in the discrete-time setting, and a novel Lyapunov–Krasovskii functional is put forward to reflect the mode-dependent time-delays. Sufficient conditions are established in terms of linear matrix inequalities (LMIs) that guarantee the existence of the state estimators. We show that both the existence conditions and the explicit expression of the desired estimator can be characterized in terms of the solution to an LMI. A numerical example is exploited to show the usefulness of the derived LMI-based conditions.This work was supported in part by the Biotechnology and Biological Sciences Research Council (BBSRC) of the UK under Grants BB/C506264/1 and 100/EGM17735, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grants GR/S27658/01 and EP/C524586/1, an International Joint Project sponsored by the Royal Society of the UK, the Natural Science Foundation of Jiangsu Province of China under Grant BK2007075, the National Natural Science Foundation of China under Grant 60774073, and the Alexander von Humboldt Foundation of Germany

Global exponential stability of generalized recurrent neural networks with discrete and distributed delays

This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2006 Elsevier Ltd.This paper is concerned with analysis problem for the global exponential stability of a class of recurrent neural networks (RNNs) with mixed discrete and distributed delays. We first prove the existence and uniqueness of the equilibrium point under mild conditions, assuming neither differentiability nor strict monotonicity for the activation function. Then, by employing a new Lyapunov–Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the RNNs to be globally exponentially stable. Therefore, the global exponential stability of the delayed RNNs can be easily checked by utilizing the numerically efficient Matlab LMI toolbox, and no tuning of parameters is required. A simulation example is exploited to show the usefulness of the derived LMI-based stability conditions.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Nuffield Foundation of the UK under Grant NAL/00630/G, and the Alexander von Humboldt Foundation of Germany

Stochastic stability of uncertain Hopfield neural networks with discrete and distributed delays

This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2006 Elsevier Ltd.This Letter is concerned with the global asymptotic stability analysis problem for a class of uncertain stochastic Hopfield neural networks with discrete and distributed time-delays. By utilizing a Lyapunov–Krasovskii functional, using the well-known S-procedure and conducting stochastic analysis, we show that the addressed neural networks are robustly, globally, asymptotically stable if a convex optimization problem is feasible. Then, the stability criteria are derived in terms of linear matrix inequalities (LMIs), which can be effectively solved by some standard numerical packages. The main results are also extended to the multiple time-delay case. Two numerical examples are given to demonstrate the usefulness of the proposed global stability condition.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Nuffield Foundation of the UK under Grant NAL/00630/G, and the Alexander von Humboldt Foundation of Germany

Microstructure and mechanical properties of wire and arc additive manufactured thin wall with low-temperature transformation

Low-temperature transformation (LTT) welding wire was initially developed to mitigate residual stress in the weld. It could also be used for internal stress optimization in Wire and Arc Additive Manufacturing (WAAM) process. In this study, a 26 layers LTT wall sample fabricated by using the WAAM technique was investigated. The microstructure of the LTT deposited wall includes elongated cellular martensite and reticular residual austenite. With the accumulation of deposition height, the prior austenite grain size increases, and the volume fraction of residual austenite and the density of dislocations in martensite decreases. According to the model of martensite transformation kinetics, the original austenite grain size is the main reason that affects the austenite fraction. In addition, the presence of a thermal cycle leads to the refinement of the martensitic microstructure and the increase in the boundary density, as well as the elimination of the sub-stable austenitic phase resulting in higher tensile properties in the middle samples than in the top ones. From the current work, it is clear that the unique thermal cycle treatment of WAAM is beneficial in improving the performance of LTT materials.</p