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• CO 2 and water composite separation method was proposed for oil separation and recovery from oily sludge. • CO 2 dissolving and swelling are beneficial to oil separating from mud. • The joint effects of CO 2 and water under proper stirring promote the oil separation from oily sludge. • The suitable operation temperature, pressure, sludge/water ratio and stirring time were 313–323 K, 3–4 MPa, 1:7 and 90 min. • Up to 80 wt% of the oil in the oily sludge could be recovered. A large amount of oily sludge is being produced in the petroleum industry each year and becoming a serious environment issue. An efficient method for separating oil from oily sludge is of great significance for disposing this hazard. In this study, a novel method—water-enhanced CO 2 extraction, was proposed to recover oil from the oily sludge. The oily sludge is mixed with water and CO 2 thus allowing the oil to be swollen by CO 2 under a certain temperature and pressure, leave the solid particles and float upwards to the top interface of the water phase owing to gravitational forces. A series of experiments were conducted to verify the method, optimize the operation conditions and compare it with the traditional approaches like the ultrasonic treatment, the solvent extraction and the supercritical extraction. The results show that up to 80 wt% of the oil in the oily sludge could be recovered using this new method, which is much higher than that using the traditional methods. The most suitable operation temperature, pressure, sludge/water ratio and stirring time were found to be 313–323 K, 3–4 MPa, 1:7 and 90 min, respectively. The proposed water-enhanced CO 2 extraction method is efficient in increasing the oil recovery of oily sludge and decreasing its operation conditions compared with traditional oily sludge treatment methods. [ABSTRACT FROM AUTHOR]

• A new modular k -out-of- n system model with functional dependency is developed. • Bayesian network is used to model the reliability of modular k -out-of- n systems. • An algorithm is designed to automatically generate the CPTs of the BN model. • DBN model is developed to update the system reliability using observations. • A real-world radar transmitter system in the space launch site is studied. The k -out-of- n systems are among the most important redundancy structures in engineering practices, and their reliability assessment has been extensively studied in the past decades. However, components in a k -out-of- n structure are often subject to functional dependency (FDEP), in which component states are affected by other components' states in the system. In this article, we study a new system structure, namely modular k -out-of- n system with FDEP. In such a system, the failure of some specific components will disable some components in the k -out-of- n structure. Bayesian network (BN) models are used to construct the structure function of modular k -out-of- n systems. The parameters encoded in the graphical structure of the modular k -out-of- n system are automatically generated by a customized algorithm. Furthermore, a dynamic BN (DBN) is developed to update the reliability of modular k -out-of- n system dynamically when observation data are collected from either component or system level. The Birnbaum importance measure of the different types of components in the modular k -out-of- n system is also evaluated by the DBN model via inserting evidence of the components' states overtime. A real-world case of a radar transmitter system in the space launch site is studied to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• A novel method water-enhanced CO 2 extraction (WECE) treating oily sludge was used. • Compare the oil recovery of WECE, supercritical CO 2 and liquid CO 2 from oily sludge at low temperature and middle pressure. • Orthogonal test method and variance analysis were used to obtain the significance of various factors and confirm operating conditions. • The sewage produced by WECE presented cleaner and better for the environment. Oily sludge produced in the petroleum industry is a hazardous waste without treatment. Oil recovery from the oily sludge can reduce environmental pollution and make recycling resources. Water-enhanced CO 2 extraction (WECE) is a novel method which could recycle oil from oily sludge efficiently, which has been proposed by our group. Although the operating conditions of WECE method had been determined by a single factor test with small experimental range in our previous work, which ignored the interaction between the other factors. This is an ideal condition which is uncertain for its actual application. In this work, correlational research for WECE method was further performed by the orthogonal test to obtain comprehensive analysis of various factors. All the experiments are tested in the visible autoclave, from which the macro morphology of treating oily sludge can be observed directly. WECE method is proved to have a better oil recovery than supercritical CO 2 and liquid CO 2 extraction at low temperature and middle pressure. In the experimental range which is larger than previous study, CO 2 pressure and system temperature are confirmed the main controlling factors of WECE which is 3 MPa and 60 ℃. From the variance results of orthogonal test, stirring time has little effect on oil recovery which could have discretionary consideration of reducing time to increase efficiency. Additionally, WECE method can dispose the oily sludge with high watercut directly. It could avoid drying process which is more economic and efficient than the traditional methods. WECE method has a potential application in the petroleum company. [ABSTRACT FROM AUTHOR]

Structure function, which quantitatively represents the relation between system states and unit states, is essential for system reliability assessment and oftentimes may not be known in advance due to complicated interactions among units. In this article, a dynamic Bayesian network (DBN) model is put forth to leverage incomplete observation sequences of hierarchical multi-state systems for structure function learning. To achieve a consistent structure function at different time instants, a customized Expectation-Maximization (EM) algorithm with parameter modularization is proposed and executed by two steps: (1) filling the missing values in the incomplete observation sequences with their expectations to break the dependencies among nodes; (2) decomposing the graphical network into V-shape structures, and then integrating the identical V-shape structures at different time slices to learn the parameters in the DBN model. Based on the learned DBN model, system state distribution and reliability function over time can be readily assessed. Two illustrative examples are presented and the results demonstrate that the structure function of a hierarchical multi-state system can be accurately learned despite the incompleteness of observation sequences. [ABSTRACT FROM AUTHOR]

• The cumulative performance of multi-state systems is evaluated. • The mission success probability is derived for infinite and finite time horizons. • Load optimization models are formulated from a cumulative performance perspective. • The computational burden is alleviated by an approximation algorithm. Multi-state is a characteristic of advanced manufacturing systems and complicated engineering systems. Multi-state systems (MSSs) have gained considerable popularity in the last few decades due to their reliability. In this study, the load optimization problem for MSSs is investigated from the perspective of cumulative performance. The cumulative performance of MSSs and the corresponding mission success probability (MSP) are formulated for both infinite and finite time horizons. The distribution of the cumulative performance of a system at failure or a particular time is evaluated using a set of multiple integrals. Correspondingly, two load optimization models are formulated to identify the optimal loading strategy for each state of an MSS to achieve the maximum MSP. As an example, a set of comparative studies are performed to demonstrate the advantages of the proposed method. The results show that (1) the proposed method can effectively evaluate the MSP from a cumulative performance perspective in a computationally efficient manner, and (2) the optimal loading strategy of an MSS can be determined by the proposed method, while varying with respect to the set amount of work to be completed and the maximum allowable mission time. [ABSTRACT FROM AUTHOR]

Abstract: A three dimensional (3D) chemical feature based pharmacophore model was developed for selective histone deacetylase 1 (HDAC1) inhibitors, which provides an efficient way to discuss the isoform selectivity of HDAC inhibitors. In contrast to the classical pan-HDAC pharmacophore, two hydrophobic features (HY and HYAr2) were found in the chemical feature based pharmacophore model, which might be responsible for the selectivity of HDAC1 inhibitions. Molecular docking also highlighted the two hydrophobic features, which are located in the internal cavity adjacent to the active site. The results contribute to our understanding of the molecular mechanism underlying the selectivity of HDAC1 inhibitors and suggest a possible target region to design novel selective HDAC1 inhibitors. [Copyright &y& Elsevier]