Modeling and analysis of rapid catalyst aging cycles

The primary mode of deactivation of automotive emission control catalysts is thermal aging, and it is well-known that high-temperature lean aging conditions are particularly detrimental. Since evaluating the long-term durability of automotive catalysts is costly and time-consuming, rapid catalyst aging cycles have been developed to mimic (in a reduced time) the catalyst deactivation under real-world driving conditions. One of the commonly used rapid catalyst aging tests is an exothermal aging cycle, which involves a combination of fuel-rich engine operation and supplemental air injection to generate high-temperature lean conditions within the catalyst bed. In this work, we use the previously developed transient three-way catalyst model to investigate the time evolution of the axial temperature profiles and exhaust air–fuel ratio (A/F) along the catalyst bed during the course of the exothermal rapid aging cycles. We find that the thermal front propagates downstream through the catalyst bed relatively slowly (compared to the concentration front) and this can limit the location within the catalyst bed and duration for high-temperature lean exposure. We also investigate how variations of some of the key system design and operating parameters can affect the extent and duration of high-temperature lean exposure. Finally, a simple analytical expression is developed which allows one to estimate the time it takes for the thermal front to travel through the catalyst bed. This time can be compared with the period of the lean A/F operation during the aging cycle to determine the location and duration of high-temperature lean exposure.     

Effect of CoFe2O4 mole percentage on multiferroic and magnetoelectric properties of Na0.5Bi0.5TiO3/CoFe2O4 particulate composites

Na_0.5Bi_0.5TiO₃ (NBT), CoFe₂O₄ (CFO) as well as particulate composites containing different mole percentages of NBT and CFO were synthesized by the solid-state sintering route and characterized for their ferroelectric and ferrimagnetic hysteresis loops, magnetostriction and magnetoelectric (ME) output. The mole% of CFO was found to influence the ferroelectric and ferrimagnetic hysteresis loops as well as magnetostriction and piezomagnetic coefficients which in turn had a significant effect on the magnetoelectric voltage coefficient. The highest magnetoelectric voltage coefficient (α) of 0.5 mV/cm/Oe was recorded in (65) NBT–(35) CFO composite.     

Experimental investigation of injection timing on the performance and exhaust emissions of a rubber seed oil blend fuel in constant speed diesel engine

An experimental study is conducted to evaluate the use of rubber seed oil with diesel at a proportion of 20% by volume (RSO20) in a constant speed (1500?rpm) direct injected four-stroke air-cooled single-cylinder compression ignition engine at different injection timings (24°, 27°, 30°, 33° bTDC (before top dead centre)). A series of tests were conducted at various engine load conditions at the rated power of 5.9?kW. The injection pressure was maintained at 200?bar. As a result of investigations, at the full load condition, the brake thermal efficiency of RSO20 at 30° bTDC is high compared with other injection timings and brake energy fuel consumption is increased when advancing injection timing. There is a significant reduction in unburned hydrocarbon emission and carbon monoxide emission, and the oxides of nitrogen emission (NO_x) is increased when advancing the injection timing.     

Tensile properties of modified 9Cr-1Mo steel by shear punch testing and correlation with microstructures

Modified 9Cr-1Mo ferritic steel (P91) is subjected to a series of heat treatments consisting of soaking for 5 min at the selected temperatures in the range 973 K–1623 K (below Ac₁ to above Ac₄) followed by oil quenching and tempering at 1033 K for 1 h to obtain different microstructural conditions. The tensile properties of the different microstructural conditions are evaluated from small volumes of material by shear punch test technique. A new methodology for evaluating yield strength, ultimate tensile strength and strain hardening exponent from shear punch test by using correlation equations without employing empirical constants is presented and validated. The changes in the tensile properties are related to the microstructural changes of the steel investigated by electron microscopic studies. The steel exhibits minimum strength and hardness when soaked between Ac₁ and Ac₃ (intercritical range) temperatures due to the replacement of original lath martensitic structure with subgrains. The finer martensitic microstructure produced in the steel after soaking at temperatures above Ac₃ leads to a monotonic increase in hardness and strength with decreasing strain hardening exponent. For soaking temperatures above Ac₄, the hardness and strength of the steel increases marginally due to the formation of soft δ ferrite.     

Nonlinear thermoacoustics of ducted premixed flames: The influence of perturbation convection speed

When a premixed flame is placed within a duct, acoustic waves induce velocity perturbations at the flame’s base. These travel down the flame, distorting its surface and modulating its heat release. This can induce self-sustained thermoacoustic oscillations. Although the phase speed of these perturbations is often assumed to equal the mean flow speed, experiments conducted in other studies and Direct Numerical Simulation (DNS) conducted in this study show that it varies with the acoustic frequency. In this paper, we examine how these variations affect the nonlinear thermoacoustic behaviour. We model the heat release with a nonlinear kinematic G-equation, in which the velocity perturbation is modelled on DNS results. The acoustics are governed by linearised momentum and energy equations. We calculate the flame describing function (FDF) using harmonic forcing at several frequencies and amplitudes. Then we calculate thermoacoustic limit cycles and explain their existence and stability by examining the amplitude-dependence of the gain and phase of the FDF. We find that, when the phase speed equals the mean flow speed, the system has only one stable state. When the phase speed does not equal the mean flow speed, however, the system supports multiple limit cycles because the phase of the FDF changes significantly with oscillation amplitude. This shows that the phase speed of velocity perturbations has a strong influence on the nonlinear thermoacoustic behaviour of ducted premixed flames.     

Big data driven jobs remaining time prediction in discrete manufacturing system: a deep learning-based approach

Implementing advanced big data (BD) analytic is significant for successful incorporation of artificial intelligence in manufacturing. With the widespread deployment of smart sensors and internet of things (IOT) in the job shop, there is an increasing need for handling manufacturing BD for predictive manufacturing. In this study, we conceive the jobs remaining time (JRT) prediction during manufacturing execution based on deep learning (DL) with production BD. We developed a procedure for JRT prediction that includes three parts: raw data collection, candidate dataset design and predictive modelling. First, the historical production data are collected by the widely deployed IOT in the job shop. Then, the candidate dataset is formalised to capture various contributory factors for JRT prediction. Further, a DL model named stacked sparse autoencoder (S-SAE) is constructed to learn representative features from high dimensional manufacturing BD to make robust and accurate JRT prediction. Our work represents the first DL model for the JRT prediction at run time during production. The proposed methods are applied in a large-scale job shop that is equipped with 44 machine tools and produces 13 types of parts. Lastly, the experimental results show the S-SAE model has higher accuracy than previous linear regression, back-propagation network, multi-layer network and deep belief network in JRT prediction.     

Processing of Thermoplastic Composites Using a Powder Slurry Technique. I. Impregnation and Preheating

The success of thermoplastic matrix composites depends upon the development of economical methods of impregnation. The purpose of this work was to develop and understand an economical impregnation procedure using a slurry based powder technology. An impregnation and preheating line consisting of a fiber tensioner, a slurry bath, a drying heater, a coating heater, and a fiber winder was used to make resin coated fibers. The influence of the process parameters on the impregnation, preheating, coating, and consolidation were studied. Part I of this paper presents the experimental investigation of the impregnation and preheating stages of the process together with a preheating model. Luikov's coupled equations of heat and mass transfer were used to model the heating and drying of the tow in the preheater. The predictions of the preheating and drying model are compared to the experimentally obtained results.     

An integrated inventory optimization model for facility location-allocation problem

This paper presents an integrated inventory distribution optimisation model for multiple products in a multi-echelon supply chain environment. Inventory, transportation and location decisions are considered. The objective is to offer practical guideline to the steel retail supply chain practitioners in choosing the correct distribution centre, finding out inventory level at individual inventory keeping points (retailers and distribution centres) point thereby helping them in reducing overall distribution cost. The framework presented endorses systems approach and suggests near-optimal approach to calculating inventory for an individual distributor and his retailers. Two algorithms are used to solve this problem, a novel hybrid Multi-objective Self-learning particle swarm optimiser and Non-dominated sorting genetic algorithm-II. The model and solution methods are tested on real data-sets obtained from organisations in the steel retail environment. The actual data on inventory holding, ordering and transportation costs of distributors and retailers are used as inputs. The decisions like choosing correct set of Distribution centres, keeping optimal regular and safety stock inventory levels are arrived at by applying practical constraints in the supply chain. Model developed assists in effective and efficient distribution of the products manufactured from the optimal location at minimal cost.     

Automated Deep Learning Based Cardiovascular Disease Diagnosis Using ECG Signals

Automated biomedical signal processing becomes an essential process to determine the indicators of diseased states. At the same time, latest developments of artificial intelligence (AI) techniques have the ability to manage and analyzing massive amounts of biomedical datasets results in clinical decisions and real time applications. They can be employed for medical imaging; however, the 1D biomedical signal recognition process is still needing to be improved. Electrocardiogram (ECG) is one of the widely used 1-dimensional biomedical signals, which is used to diagnose cardiovascular diseases. Computer assisted diagnostic models find it difficult to automatically classify the 1D ECG signals owing to time-varying dynamics and diverse profiles of ECG signals. To resolve these issues, this study designs automated deep learning based 1D biomedical ECG signal recognition for cardiovascular disease diagnosis (DLECG-CVD) model. The DLECG-CVD model involves different stages of operations such as pre-processing, feature extraction, hyperparameter tuning, and classification. At the initial stage, data pre-processing takes place to convert the ECG report to valuable data and transform it into a compatible format for further processing. In addition, deep belief network (DBN) model is applied to derive a set of feature vectors. Besides, improved swallow swarm optimization (ISSO) algorithm is used for the hyperparameter tuning of the DBN model. Lastly, extreme gradient boosting (XGBoost) classifier is employed to allocate proper class labels to the test ECG signals. In order to verify the improved diagnostic performance of the DLECG-CVD model, a set of simulations is carried out on the benchmark PTB-XL dataset. A detailed comparative study highlighted the betterment of the DLECG-CVD model interms of accuracy, sensitivity, specificity, kappa, Mathew correlation coefficient, and Hamming loss.