Sealing behaviour of glass-based composites for oxygen transport membranes

In this study, seven different filler materials in different proportions were added to a Ba-Ca-Si glass matrix “H” to investigate new sealant with higher thermal expansion coefficient (CTE) value and good sealing performance for application in oxygen transport membrane (OTM). SrTi_0.75Fe_0.25O_3-δ (STF25) was used as an OTM, and the sealing partners were ferritic steel Aluchrom and pre-oxidized Aluchrom. Compatibility tests were carried out to investigate the feasibility of the composites. Higher CTE values were found in dilatometer tests on composite samples by adding 40 wt% Ag (HAg40) and 30 wt% Ni-Cr (HNC30). Gas-tightness measurements of sandwiched samples produced appropriate helium leakage rates in the range of 10^?6 mbar·l·s^?1. Sealing behaviour of sealants HAg40 and HNC30 were investigated by joining STF25 and as-delivered/pre-oxidized Aluchrom together. Scanning electron microscopy (SEM) on cross-sections of the joints revealed a homogeneous microstructure and good adherence of the glass sealants to support metals and STF25.     

Approximation Optimal Vibration Control with Applications to Jacket Offshore Platforms with Nonlinear Dynamic Characteristics

International Journal of Control, Automation and Systems - The vibration control problem of offshore jacket platforms is studied. The model of offshore platforms with nonlinear interactions of the...     

Transmission system restoration with co-optimization of repairs,load pickups,and generation dispatch

This paper studies the restoration of a transmission system after a significant disruption such as a natural disaster. It considers the co-optimization of repairs, load pickups, and generation dispatch to produce a sequencing of the repairs that minimizes the size of the blackout over time. The core of this process is a Restoration Ordering Problem (ROP), a non-convex mixed-integer nonlinear program that is outside the capabilities of existing solver technologies. To address this computational barrier, the paper examines two approximations of the power flow equations: The DC model and the recently proposed LPAC model. Systematic, large-scale testing indicates that the DC model is not sufficiently accurate for solving the ROP. In contrast, the LPAC power flow model, which captures line losses, reactive power, and voltage magnitudes, is sufficiently accurate to obtain restoration plans that can be converted into AC-feasible power flows. An experimental study also suggests that the LPAC model provides a robust and appealing tradeoff between accuracy and computational performance for solving the ROP.     

Output-relevant Variational autoencoder for Just-in-time soft sensor modeling with missing data

Main challenges for developing data-based models lie in the existence of high-dimensional and possibly missing observations that exist in stored data from industry process. Variational autoencoder (VAE) as one of the deep learning methods has been applied for extracting useful information or features from high-dimensional dataset. Considering that existing VAE is unsupervised, an output-relevant VAE is proposed for extracting output-relevant features in this work. By using correlation between process variables, different weight is correspondingly assigned to each input variable. With symmetric Kullback–Leibler (SKL) divergence, the similarity is evaluated between the stored samples and a query sample. According to the values of the SKL divergence, data relevant for modeling are selected. Subsequently, Gaussian process regression (GPR) is utilized to establish a model between the input and the corresponding output at the query sample. In addition, owing to the common existence of missing data in output data set, the parameters and missing data in the GPR are estimated simultaneously. A practical debutanizer industrial process is utilized to illustrate the effectiveness of the proposed method.     

Heterometallic sulfide cluster [Ag6Sn6S20]10 −: Solvothermal syntheses and characterizations of silver thiostannates with lanthanide complex counter cations

Novel organic hybrid silver thiostannates [Hen]₄[Ln(en)₄]₂[Ag₆Sn₆S₂₀]·3en (Ln = Er, 1; Tm, 2; Yb, 3) were prepared by the reactions of Ln₂O₃, Ag, Sn and S in ethylenediamine (en) under solvothermal conditions. Six SnS₄ tetrahedra and six AgS₃ triangles are connected into the heterometallic sulfide cluster [Ag₆Sn₆S₂₀]^10 − via edge-sharing. In the [Ag₆Sn₆S₂₀]^10 − cluster, a hexanuclear Ag₆S₆ core is enclosed by two Sn₃S₁₀ fragments. The Ag₆S₆ core is the first As–S cluster stabilized by inorganic SnS₄ ligands. In 1–3, all Ln^3 + ions are in 8-fold coordination environments that involved four bidentate en ligands, forming bicapped trigonal prisms. Compounds 1–3 show well-defined absorption edges with band gaps in the range of 2.18–2.47 eV.     

Parallel chaos optimization algorithm with migration and merging operation

Chaos optimization algorithm (COA) utilizes the chaotic maps to generate the pseudo-random sequences mapped as the decision variables for global optimization applications. A kind of parallel chaos optimization algorithm (PCOA) has been proposed in our former studies to improve COA. The salient feature of PCOA lies in its pseudo-parallel mechanism. However, all individuals in the PCOA search independently without utilizing the fitness and diversity information of the population. In view of the limitation of PCOA, a novel PCOA with migration and merging operation (denoted as MMO-PCOA) is proposed in this paper. Specifically, parallel individuals are randomly selected to be conducted migration and merging operation with the so far parallel solutions. Both migration and merging operation exchange information within population and produce new candidate individuals, which are different from those generated by stochastic chaotic sequences. Consequently, a good balance between exploration and exploitation can be achieved in the MMO-PCOA. The impacts of different one-dimensional maps and parallel numbers on the MMO-PCOA are also discussed. Benchmark functions and parameter identification problems are used to test the performance of the MMO-PCOA. Simulation results, compared with other optimization algorithms, show the superiority of the proposed MMO-PCOA algorithm.     

An investigation into the cause of loss of containment from the supply of mini-bulk lubricants

An Intermediate Bulk Container (IBC) was punctured during its handling, releasing oil onto soil at an environmentally-sensitive region of Australia. The telehandler did not pierce the plastic of the IBC directly (as was expected) but rather one of the tynes had caught on the underside of the metal base plate, despite numerous controls being in place at time of spill, revealing a previously unreported mechanism for a fluid spill from handling of petroleum hydrocarbons. The diverse investigation team used a root cause analysis (RCA) technique to identify the underlying cause: the inspection process was inadequate with contributing factors of not using a spotter and design of IBC did not anticipate conditions. Engineering controls were put in place as part of the change management process to help prevent spills from occurring from piercing from telehandler tynes on the current project site.     

A bi-layer optimization approach for a hybrid flow shop scheduling problem involving controllable processing times in the steelmaking industry

A steelmaking-continuous casting (SCC) scheduling problem is an example of complex hybrid flow shop scheduling problem (HFSSP) with a strong industrial background. This paper investigates the SCC scheduling problem that involves controllable processing times (CPT) with multiple objectives concerning the total waiting time, earliness/tardiness and adjusting cost. The SCC scheduling problem with CPT is seldom discussed in the existing literature. This study is motivated by the practical situation of a large integrated steel company in which the just-in-time (JIT) and cost-cutting production strategy have become a significant concern. To address this complex HFSSP, the scheduling problem is decomposed into two subproblems: a parallel machine scheduling problem (PMSP) in the last stage and an HFSSP in the upstream stages. First, a hybrid differential evolution (HDE) algorithm combined with a variable neighborhood decomposition search (VNDS) is proposed for the former subproblem. Second, an iterative backward list scheduling (IBLS) algorithm is presented to solve the latter subproblem. The effectiveness of this bi-layer optimization approach is verified by computational experiments on well-designed and real-world scheduling instances. This study provides a new perspective on modeling and solving practical SCC scheduling problems.