Superabsorbent polymer prepared using carboxymethyl cellulose derived from Ceiba pentandra (L.) Gaertn. (kapok) cotton

Superabsorbent polymers (SAPs) were synthesized by grafting acrylic acid and butyl acrylate onto carboxymethyl cellulose (CMC) modified from Ceiba pentandra (L.) Gaertn. (kapok) cotton, with N,N′‐methylenebisacrylamide as a crosslinker and ammonium persulphate as initiator. The effect of distilled water, saline solution, and applied pressure on superabsorbent was investigated. The product exhibited the maximum water absorbency of 554 g/g in distilled water and 96 g/g in saline solution. The SAP achieved the highest water absorbency under load of 83 g/g under applied pressure of 7.6 g/cm². The kapok cotton modified cellulose‐based SAP exhibited stronger gel strength than the SAP based on commercial CMC. This is probably due to the higher grafting efficiency (78.3%) of the former. The SAP was characterized by FTIR analysis, thermogravimetric analysis, and scanning electron microscopy. Thermogravimetric analysis results showed that the SAP, with AA and BA grafted onto CMC, had better thermal stability than CMC alone. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 40808.     

Performance measurement of product returns with recovery for sustainable manufacturing

Today's lifecycle of new and emerging products, increase in labour costs in developed countries and user's expectations or behaviours including frequently upgrading items with latest features, influence the growth rate of product disposal to landfill. To reduce the negative impact on the environment, global manufacturers need to take responsibility for designing sustainable products and implementing cleaner production systems for 3R operations (3R–Reuse/Remanufacture/Recycle). Nevertheless, there is still a lack of comprehensive measures for assessing product returns with recovery settings. In this paper, a framework for performance evaluation using design for six sigma methodology is developed to estimate utilisation value of a manufactured product with recovery settings, which accounts for total recovery cost, manufacturing lead-time, minimisation for landfill waste and quality characteristic. Finally, a numerical example based on these performance attributes to assess product utilisation value is presented.     

TOWARD A CREATIVE PROBLEM-SOLVING METHODOLOGY WITH KNOWLEDGE PROVISION

In this article, a knowledgeable creative problem-solving methodology (KCPS) is proposed. In addition to being applied as a creative problem-solving methodology, this KCPS method also aims to provide human users the knowledge that is important and necessary for the generation of creative solutions. The development of KCPS is based on the research achievement in the area of human creativity, which suggests that knowledge plays a crucial role in the evolvement of creativity in human mind. Consequently, knowledge provision is one of the key components of the KCPS method. Additionally, according to the models of human creativity, information processing and Meta-behaviors are identified as the other two components of the KCPS. Information processing facilitates human users in analyzing, processing information, and obtaining knowledge in order to approach problems creatively. Meta-behaviors indicate the mental activities of human users when they apply the KCPS method. Subsequently, appropriate techniques that can realize the functions of knowledge provision and information processing are selected and integrated for the development of the KCPS. The operation of the KCPS method is then discussed.

A case study is illustrated with twelve new design concepts for articles of balancing equipment, which were generated by applying the KCPS method. This case study demonstrates that the KCPS method has the capacity to assist human users for the generation of innovative concepts.     

Simple synthesis of ZnS nanoparticles in alkaline medium

ZnS nanoparticles were successfully synthesized by reflux under an alkaline medium. The nanoparticles were characterized by using X-Ray diffraction (XRD), scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The optical properties of ZnS nanoparticles were examined by photoluminescence (PL) spectrum. The result shows that the as-synthesized ZnS nanoparticles had a cubic phase. SEM image shows that ZnS nanoparticles are basically in spherical shape and are homogeneous. The particle size was found to be in the range of 18 nm.     

Properties of tin sulphide thin films electrodeposited in the presence of triethanolamine

Tin sulphide thin films have been synthesized in aqueous media in the presence of triethanolamine using electrodeposition method. The effect of deposition potential, triethanolamine concentration and deposition time towards the properties of tin sulfide films was studied. The presence of triethanolamine showed improvement in reproducibility, adherence and crystallinity of the films. The X-ray diffraction studies indicated formation of polycrystalline compounds. The properties of the films varied with the variation in the deposition parameters. The highest photoresponse was obtained for the film deposited at –0.80 V in the presence of 0.06 M triethanolamine. Films deposited at longer deposition time showed higher photoresponse. The absorbance study revealed that the bandgap energy was about 1.20 eV with indirect transition.     

Development of a parallel processing multi-agent architecture for a virtual CIM system

Global manufacturing enterprises tend to rely on fully integrated manufacturing systems to satisfy constantly changing market requirements. In addition, small to medium enterprises are in a dilemma when competing with large organizations. Virtual computer-integrated manufacturing (VCIM) is a realistic concept that can provide the integration requirements for the globally distributed manufacturing resources and it has the potential to satisfy the market requirements of small to medium enterprises. The vision of the VCIM is to improve the efficiency and effectiveness of manufacturing enterprises by seamlessly integrating globally distributed manufacturing resources as much as possible. Meanwhile, intelligent agent technology provides a better means to implement distributed components as integrated application systems. The paper provides a parallel processing multi-agent architecture to support a global integrated manufacturing system in the form of the VCIM. In this architecture, a three-layered structure is proposed to accommodate all the agents no matter where they are located. Multiple Facilitator agents are proposed with similar functionalities to smooth the information flow across the integrated system in a parallel connection manner. In addition, a multi-agent VCIM model that describes the agent identification approaches for VCIM, Java environment implementation approaches and a simulation system to demonstrate the parallel processing multi-agent architecture are also discussed.     

Investigation on the effect of concentration of methane in biogas when used as a fuel for a spark ignition engine

The influence of reduction in the concentration of CO₂ in biogas on performance, emissions and combustion in a constant speed spark ignition (SI) engine was studied experimentally. A lime water scrubber was used to lower carbon dioxide (CO₂) levels from 41% in biogas to 30% and 20%. The tests covered the range of equivalence ratios from rich to the lean operating limit at a constant speed of 1500 rpm and at compression ratio of 13:1 with a masked valve to enhance swirl. With a reduction in the CO₂ level there was a significant improvement in the performance and reduction in emissions of hydrocarbons (HC) particularly with lean mixtures. The lean limit of combustion also gets extended. Heat release rates indicated enhanced combustion rates, which are mainly responsible for the improvement in thermal efficiency. A reduction in the CO₂ level by 10% seemed to be sufficient for reducing HC levels and the NO levels were also not significantly raised. The spark timings were to be retarded by about 5° when the CO₂ concentration was decreased by 10%.     

Experimental design to predict process variables in the preparation of cellulose based sustained release microspheres system loaded with prednisolone-cyclodextrin complex

The purpose of this study was to evolve experimental design, to prepare the sustained release microspheres loaded with prednisolone-hydroxypropyl-β-cyclodextrin complex, and develop a successful mathematical model to predict various characteristics of microspheres. Response surface methodology (RSM) has been employed to develop model equations that correlate process variables such as ethyl cellulose (EC, mg), hydroxypropyl methyl cellulose (HPMC, mg), stirring speed (rpm) and surfactant (%) with the response variables such as entrapment efficiency (%), particle size (μm) and release rate (%) of the drug. The adequacy of model equations is confirmed by ANOVA result. Results as predicted by model equations are in good agreement with that of experimental results. In vitro drug release shows that drug (93%) is released from a check point formulation (CPF 2) over the period of 24 h with a sustained release fashion with Quasi-Fickian kinetics. Surface morphology of microspheres varies with the experimental conditions as evidenced by scanning electron microscopy.     

Multi-Site Air Pollutant Prediction Using Long Short Term Memory

The current pandemic highlights the significance and impact of air pollution on individuals. When it comes to climate sustainability, air pollution is a major challenge. Because of the distinctive nature, unpredictability, and great changeability in the reality of toxins and particulates, detecting air quality is a puzzling task. Simultaneously, the ability to predict or classify and monitor air quality is becoming increasingly important, particularly in urban areas, due to the well documented negative impact of air pollution on resident’s health and the environment. To better comprehend the current condition of air quality, this research proposes predicting air pollution levels from real-time data. This study proposes the use of deep learning techniques to forecast air pollution levels. Layers, activation functions, and a number of epochs were used to create the suggested Long Short-Term Memory (LSTM) network based neural layer design. The use of proposed Deep Learning as a structure for high-accuracy air quality prediction is investigated in this research and obtained better accuracy of nearly 82% compared to earlier records. Determining the Air Quality Index (AQI) and danger levels would assist the government in finding appropriate ways to authorize approaches to reduce pollutants and keep inhabitants informed about the findings.     

Simultaneous reduction of NO–smoke–CO<Subscript>2</Subscript> emission in a biodiesel engine using low-carbon biofuel and exhaust after-treatment system

The present work focuses on the simultaneous reduction of NO–smoke–CO₂ emission in a Karanja oil methyl ester (KOME)-fueled single-cylinder compression ignition engine by using low-carbon biofuel with exhaust after-treatment system. Replacement of KOME for diesel reduced smoke emission by 3% but resulted in increase of NO emission and CO₂ emission by 13 and 35% at 100% load condition. In order to reduce CO₂ emission, tests were conducted with a blend of KOME and orange seed oil (OSO), a low-carbon fuel on equal volume basis (50–50). At the same operating conditions, compared to KOME, 27% reduction in CO₂ emission and 5% reduction in smoke emission were observed. However, a slight increase in NO emission was observed. To achieve simultaneous reduction of NO–smoke–CO₂ emissions, three catalysts, namely monoethanolamine, zeolite and activated carbon, were selected for exhaust after-treatment system and tested with optimum KOME–OSO blend. KOME–OSO + zeolite showed a great potential in simultaneous reduction of NO–smoke–CO₂ emissions. NO, smoke and CO₂ emissions were simultaneously reduced by about 15% for each emission compared to diesel at 100% load condition. The effect of exhaust after-treatment system with KOME–OSO blend on combustion, performance and other emission parameters is discussed in detail in this study. Fourier transform infrared spectrometry analysis and testing were done to identify the absorbance characteristics of zeolite material.