Process modeling and optimization of industrial ethylene oxide reactor by integrating support vector regression and genetic algorithm

This article presents an artificial intelligence‐based process modeling and optimization strategies, namely support vector regression–genetic algorithm (SVR‐GA) for modeling and optimization of catalytic industrial ethylene oxide (EO) reactor. In the SVR‐GA approach, an SVR model is constructed for correlating process data comprising values of operating and performance variables. Next, model inputs describing process operating variables are optimized using Genetic Algorithm (GAs) with a view to maximize the process performance. The GA possesses certain unique advantages over the commonly used gradient‐based deterministic optimization algorithms The SVR‐GA is a new strategy for chemical process modeling and optimization. The major advantage of the strategies is that modeling and optimization can be conducted exclusively from the historic process data wherein the detailed knowledge of process phenomenology (reaction mechanism, kinetics, etc.) is not required. Using SVR‐GA strategy, a number of sets of optimized operating conditions leading to maximized EO production and catalyst selectivity were obtained. The optimized solutions when verified in actual plant resulted in a significant improvement in the EO production rate and catalyst selectivity.     

On Composite Leakage Current Maximization

Sub-threshold, gate and reverse biased junction band-to-band tunneling leakage currents depend on the logic inputs of a CMOS circuit. In this paper, we consider all leakage currents together and generate pattern with the objective of maximizing the overall leakage current to avoid any optimism in leakage current estimation. The computation involves Boolean reasoning on a pre-characterized set of interconnected gates. This problem is known to be computationally intractable. We propose a heuristic with reduced complexity by looking for a lower and an upper bound instead. The bounds tighten progressively with computation and converge asymptotically on a provably exact solution. By appropriately setting the objective function, the same algorithm may also be applied to find the pattern that minimizes the leakage power in the system idle state, which is considered to be ∼50% of the total power consumed in the current technology generations.

Structure,properties, and applications of polyacrylonitrile/carbon nanotube (CNT) fibers at low CNT loading

Bi-component, polyacrylonitrile (PAN)/carbon nanotube (CNT) fibers were processed, at different core-sheath area ratios, by gel spinning. A percolated CNT network at 10 wt% CNT in the sheath enhanced electrical conductivity as compared to the neat PAN fiber, while PAN polymer in the core contributed to the good mechanical properties. Fibers with relatively thin sheath allowed overall CNT loading as low as 3.7 wt% to be made with good electrical conductivity, and PAN stabilization by Joule heating was demonstrated. Such fibers with combined good mechanical properties and electrical conductivity can also potentially be used for electrical heating of fabrics, for making smart textiles, and for electromagnetic interference shielding.     

A review on modeling of solar photovoltaic systems using artificial neural networks,fuzzy logic,genetic algorithm and hybrid models

The uncertainty associated with modeling and performance prediction of solar photovoltaic systems could be easily and efficiently solved by artificial intelligence techniques. During the past decade of 2009 to 2019, artificial neural network (ANN), fuzzy logic (FL), genetic algorithm (GA) and their hybrid models are found potential artificial intelligence tools for performance prediction and modeling of solar photovoltaic systems. In addition, during this decade there is no extensive review on applicability of ANN, FL, GA and their hybrid models for performance prediction and modeling of solar photovoltaic systems. Therefore, this article focuses on extensive review on design, modeling, maximum power point tracking, fault detection and output power/efficiency prediction of solar photovoltaic systems using artificial intelligence techniques of the ANN, FL, GA and their hybrid models. In addition, the selected articles on the solar radiation prediction using ANN, FL, GA and their hybrid models are also summarized. Total of 122 articles are reviewed and summarized in the present review for the period of 2009 to 2019 with 90 articles in the field of {ANN, FL, GA and their hybrid models} + solar photovoltaic systems and 32 articles in the field of {ANN, FL, GA and their hybrid models} + solar radiation. The review shows the suitability and reliability of ANN, FL, GA and hybrid models for accurate prediction of the solar radiation and the performance characteristics of solar photovoltaic systems. In addition, this review presents the guidance for the researchers and engineers in the field of solar photovoltaic systems to select the suitable prediction tool for enhancement of the performance characteristics of the solar photovoltaic systems and the utilization of the available solar radiation.     

Development of sensitive and specific multiplex PCR method for the detection of microcystin producing cyanobacteria in spirulina food supplements

Food Science and Biotechnology - Spirulina has emerged as the next-generation dietary supplement owing to its health benefits. Despite the advantages, there have been reports of contamination by...     

Dithieno[3,2‐b:2′,3′‐d]pyrrol‐Cored Hole Transport Material Enabling Over 21% Efficiency Dopant‐Free Perovskite Solar Cells

Dopant‐free hole transport materials (HTMs) are essential for commercialization of perovskite solar cells (PSCs). However, power conversion efficiencies (PCEs) of the state‐of‐the‐art PSCs with small molecule dopant‐free HTMs are below 20%. Herein, a simple dithieno[3,2‐b:2′,3′‐d]pyrrol‐cored small molecule, DTP‐C6Th, is reported as a promising dopant‐free HTM. Compared with commonly used spiro‐OMeTAD, DTP‐C6Th exhibits a similar energy level, a better hole mobility of 4.18 × 10^?4 cm² V^?1 s^?1, and more efficient hole extraction, enabling efficient and stable PSCs with a dopant‐free HTM. With the addition of an ultrathin poly(methyl methacrylate) passivation layer and properly tuning the composition of the perovskite absorber layer, a champion PCE of 21.04% is achieved, which is the highest value for small molecule dopant‐free HTM based PSCs to date. Additionally, PSCs using the DTP‐C6Th HTM exhibit significantly improved long‐term stability compared with the conventional cells with the metal additive doped spiro‐OMeTAD HTM. Therefore, this work provides a new candidate and effective device engineering strategy for achieving high PCEs with dopant‐free HTMs.     

Homogeneous and polymorphic transformations to ordered intermetallics in nanostructured Au–Cu multilayer thin films

Journal of Materials Science - Atomic arrangements in the nanostructured grains and interfaces of thermally evaporated Au/Cu multilayer thin films on polycrystalline Si substrate have been explored...