The pressure of a distillation column for regenerating an absorbent is one of the key variables for minimizing the energy in a carbon capture and storage (CCS) chain. The steam drag point in power plant, the regeneration energy in capture process and the compression energy in the liquefaction process are highly dependent on the pressure of regeneration column . In this work, a new method for determining optimal value of stripper pressure is proposed based on the integrated simulation model and sequential optimization method. Total required energy has been represented as a function of the stripper pressure and the terminal pressure. The results show that the higher pressure is generally recommended to reduce the required energy, revealing that the value of the stripper pressure depends on the liquefaction process more than the power plant.
We developed a flexible two-ply piezoelectric yarn-type generator using an electrospun polyvinylidenefluorideco- trifluoroethylene (PVDF–TrFE) mat and a commercially available silver-coated nylon fiber. By rolling the silvercoated nylon fiber into the electrospun PVDF–TrFE mat as the inner electrode, the two-dimensional piezoelectric PVDF– TrFE mat was easily transformed into a one-dimensional fiber. Then silver-coated nylon fiber rolled in PVDF–TrFE was plied with another similar fiber to make a flexible two-ply piezoelectric yarn. The overall fabrication processes of the flexible two-ply piezoelectric yarn are simple and have a high application potential. The flexible two-ply piezoelectric yarn can generate up to 0.7 V in compression and 0.55 V in tension. The yarn retained the piezoelectric performance in various shapes, such as a sewn structure. In addition, the piezoelectric performance was sensitive to velocity and pressure. The flexible two-ply piezoelectric yarn has potential applications as a human motion sensor, as a building block of energy- harvesting textiles, and in self-powered biomedical applications. Keywords: Energy harvester, Flexible, Piezoelectric, Two-ply, Wearable, Yarn.
We fabricate an efficient triboelectric generator (TEG) using inexpensive materials that are readily available in our surroundings. By casting PDMS, we perform micropatterning on the surface of sandpaper.
In this study, at first a hybrid local fault diagnostic model based on the signed digraph (SDG) which is a kind of model based approaches and a statistical learning model, support vector machine (SVM), would be proposed. And then, the fault intensity model and the fault boundary model were constructed for various fault intensities. Key aspects are the issue of resolving signatures resulting from the same fault but with differing intensities and making the decision tool to decide which a fault occurs.
Various multivariate statistical methods based on pattern recognitions for the Tennessee Eastman (TE) process have been developed to identify and diagnose the root cause of assumed faults. However, even if the same fault occurs, its patterns or traces generated from such conventional approaches can be different according to fault magnitudes. Thus, the fault magnitude should be considered. In this study, a signed digraph (SDG) based on process knowledge is used to identify the relationships between process variables and conceivable faults. A support vector regression (SVR) and dynamic independent component analysis (DICA) are then applied to construct empirical models as a function of process variables associated with assumed faults and their fault magnitudes for isolating a fault and handling non-Gaussian information. In addition, empirical models for predicting fault magnitudes are constructed. The efficacy of the proposed approach is illustrated by comparing it with the previous studies applied for the benchmark process.
Computational methods for designing an optimal catalyst have recently been gaining more popularity in the fields of catalysis and reaction engineering of energy systems. However, in general, the problem in these approaches is that uncertainties present in process models should be handled correctly to achieve a robust design. To find the optimal design under these uncertainties, a stochastic optimization method can be employed. In this work, the optimal properties of a catalyst for ammonia decomposition to produce hydrogen are investigated, and uncertainties associated with the reactions and their parameters are modeled as exogenous uncertain variables which follow known probability distributions. The goal of this work is to find the optimal binding energies of the catalyst that maximize conversion of ammonia in a microreactor. Our stochastic optimization problem is nonlinear, and involves the expectation operator as well as integration in the objective function. To tackle this complex system, the expectation of conversion based on a sample average approximation (SAA) method is evaluated. However, the exponential increase in the number of samples to be considered with the number of uncertain parameters lead to severe computational problems when using all possible combinations of the uncertain parameters. To solve this, linearity analysis, together with partial least squares, is implemented to reduce the number of uncertain parameters. In the optimization step, a particle swarm optimization (PSO) is employed. The results indicate that the stochastic optimum shows higher conversion and different optimal binding energies than the deterministic optimum, and is a more robust solution.
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