High performance blended membranes using a novel preparation technique

The possibility of applying novel microwave (MW) technique in the dissolution of polyethersulfone (PES) and lithium halides in aprotic solvent is studied. The lithium halides additives used are lithium fluoride (LiF), lithium bromide (LiBr) and lithium chloride (LiCl) and a comparison is made with conventional method. PES was dissolved in dimethylformamide (DMF) in the single solvent whilst for the double solvent (DS); PES was dissolved in a mixture of two different solvents DMF and acetone. The concentrations of lithium halide in both solvents were varied from 1 to 5 wt%. In order to illuminate the mechanism through which lithium halide influences the kinetic membrane performance in both techniques, rheological, FTIR, contact angle and water uptake analysis were performed. The performances of the membranes were evaluated in terms of pure water permeation (PWP), permeation rate (PR) and separation rates of various polyethylene glycols. Result revealed that the hollow fiber MW membrane with the 3 wt% LiBr additive exhibits both high permeation rates of 222.16 Lm-2hr-1 and separation rates of 99% and molecular weight cutoff (MWCO) of 2.6 kDa. In general, the MW membranes exhibited higher permeation and separation rates compared to conventional electrothermal heating (CEH) membranes. The FTIR, contact angle and water uptake measurement revealed that the LiCl and LiBr have enhanced the hydrophilic properties of the PES membranes thus producing membrane with high permeation and separation rates

Sustainability of product life cycle: a case study of hollow fiber membrane

The world’s supply of fresh water is finite and threatened by pollutions such wastewater distributed from industries. Membrane technology has received increasing attention for the wastewater treatment lately [Kumar et al., 2014; Melin et al., 2006]. The membrane function performance usually identified, while the performance from sustainability perspective have received less attention.In line with the Malaysian government policy, the membrane system used for wastewater treatment needs to be scrutinized to ensure sustainability according to Triple Bottom Line aspects including environmental, social and economic. However, this paper focusing on environmental sustainability aspects. The potential environmental burdens calculated are global warming potential (GWP), acidification potential (AP), eutrophication potential (EP) and waste potential (EP)

Optimization of roundness error in deep hole drilling using cuckoo search algorithm

In the manufacturing industry, machining is a part of all manufacture in almost all metal products. Machining of holes is one of the most common processes in the manufacturing industries. Deep hole drilling, DHD is classified as a complex machining process .This study presents an optimization of machining parameters in DHD using Cuckoo Search algorithm, CS comprising feed rate (f), spindle speed (s), depth of hole (d) and Minimum Quantity Lubrication MQL, (m). The machining performance measured is roundness error, Re. The real experimentation was designed based on Design of Experiment, DoE which is two levels full factorial with an added centre point. The experimental results were used to develop the mathematical model using regression analysis that used in the optimization process. Analysis of variance (ANOVA) and Fisher‘s statistical test (F-test) are used to check the significant of the model developed. According to the results obtained by experimental the minimum value of Re is 0.0222μm and by CS is 0.0198μm. For the conclusion, it was found that CS is capable of giving the minimum value of Re as it outperformed the result from the experimental

Low carbon emissions review in aviation engine technology for Asean airspace: a proposal

Air tranportation has been major mode of connecting role player in business supply chain and catalyst for tourism industry. Emission from aircraft engines do produce various gasses as by product of combustion. Thus in this paper the authors focus on gas turbine engine emissions in accordance to ICAO Annex 16 emission protection recommendations. Majority of the aircraft are fitted with gas turbine engines for propulsion. The United States Federal Aviation Administration (FAA) and European Aviation Safety Agency (EASA) emission documents were studied. Cabon in this context is CO2 directly related to fuel consumption by the engine. The engine emission consist of NOx , HC, CO2 and smoke. However with various initiatives aircraft today 15 percent reduction in fuel burn, 40 percent lower in emission compared to a decade ago (ICAO Environmental report 2010) The FAA have launched the continuous lower energy, emissions and noise (CLEEN) program in 2010. The goal of the CLEEN program is to noise and emissions reduction by aircraft and engine technology. Another improvement was the alternative fuel deployment and development project. This project known as “Farm to Fly” and base on this initiative development three alternative jet fuels approved for aviation use by ASTM standards. FAA also developing a proposed Global Market Based Measure (GMBM) for metrics of measurement. These holistic program known as “Next Generation Air Transportation System” or NextGen. NextGen has five pillars of strategy. (US Aviation GHG reduction plan 2015)

A conceptual sustainable domain value stream mapping framework for manufacturing

Part of: Seliger, Günther (Ed.): Innovative solutions : proceedings / 11th Global Conference on Sustainable Manufacturing, Berlin, Germany, 23rd - 25th September, 2013. - Berlin: Universitätsverlag der TU Berlin, 2013. - ISBN 978-3-7983-2609-5 (online). - http://nbn-resolving.de/urn:nbn:de:kobv:83-opus4-40276. - pp. 54-59.Adoption of lean manufacturing generally involves waste reduction and its adoption has been successful in improving companies. With increasing awareness on the need for sustainable development, works have been done on sustainability assessment of product design and manufacturing processes. The sustainable manufacturing, 6R method can be adopted to improve the existing design and manufacturing sustainability scores. A conceptual hybrid framework integrating lean manufacturing with sustainable manufacturing theories has been developed thus enabling the benefits from both techniques to be gained. Specifically, the lean manufacturing, value stream mapping tool is integrated with the sustainable manufacturing, 6R method to assist in solving manufacturing problems at process and or plant level sustainably. An indicator, providing the sustainability scores on value adding and non value adding elements at present and future state, has been proposed as part of the framework

Qualitative theory building method for lean sustainable framework development: a methodology

Research in manufacturing engineering was commonly conducted by employing quantitative methodology. Experimental and survey questionnaire methods are widely used by researchers. Hence, this paper will explain the justification for using the mix method of grounded theory and survey questionnaires to develop a lean sustainable framework. The authors used a non conventional method in mechanical engineering to develop the framework. The research method was classified as interpretative method. The grounded theory method is used to link the collected data until the emergence of an inductive theory generation. The rigor of the research was based on triangulation. Triangulation method improves the reliability and validity of the research. The end product of this research was the development of lean sustainable framework

A Review of Minimum Quantity Lubrication Technique with Nanofluids Application in Metal Cutting Operations

Minimum quantity lubrication (MQL) technique did not only serve as a better alternative to flood cooling during machining but enhance better surface finish, minimizes the cost, reduces the impact loads on the environment and health hazards on the operation personnel. However, the coolant or lubrication media used in MQL technique posed certain restrictions especially at very high cutting speeds where the lubricating oil tends to evaporates as it strikes the already heated cutting tool at elevated temperature. Desire to compensate for the shortcomings of the lubricating media in the MQL technique led to the introduction of nanoparticles in the cutting fluids for use in the MQL lubrication process. Nanoparticles have much higher and stronger temperature-dependent thermal conductivity and enhanced heat transfer coefficient at very low particle concentration, which are key parameters for their enhanced performance in many of the machining applications. Optimizing the nanoparticles concentration leads to efficiency in most of their application. Their ball bearing effect lubrication at the cutting zone through formation of film layer which reduces friction between the contact surfaces thereby reducing cutting force, temperature and tool wear. It has been reported in various studies that nanoparticles introduction in cutting fluids led to excellent machining performance in reduction of cutting forces, reduced tool wear, reduced cutting temperature and improved surface finish of the work piece thereby increasing productivity and reduction of hazards to the health of personnel and the environment better than the pure or conventional MQL process. Thus, the application of various nanoparticles and its performances in metal cutting operations with respect to the cutting forces, surface finish, tool wear and temperature at the cutting zone are evaluated and highlighted

Determination of energy consumption during turning of hardened stainless steel using resultant cutting force

Downsizing energy consumption during the machining of metals is vital for sustainable manufacturing. As a prerequisite, energy consumption should be determined, through direct or indirect measurement. The manufacturing process of interest is the finish turning which has been explored to generate (near) net shapes, particularly for hardened steels. In this paper, we propose using measured cutting forces to calculate the electrical energy consumption during the finish turning process of metals where typically the depth of cut is lower than the cutting tool nose radius. In this approach, the resultant cutting force should be used for calculating the energy consumption, instead of only the main (tangential) cutting force as used in the conventional approach. A case study was carried out where a hardened stainless steel (AISI 420, hardness of 47–48 HRC) was turned using a coated carbide tool, with a nose radius of 0.8 mm, without cutting fluid, and at 0.4 mm depth of cut. The experimental design varied the cutting speed (100, 130, and 170 m/min) and feed (0.10, 0.125, and 0.16 mm) while other parameters were kept constant. The results indicate that the electrical energy consumption during the particular dry turning of hardened steel can be calculated using cutting force data as proposed. This generally means machining studies that measure cutting forces can also present energy consumption during the finish or hard turning of metals, without specifically measuring the power consumption of the machining process. For this particular dry turning of hardened stainless steel, cutting parameters optimization in terms of machining responses (i.e., low surface roughness, long tool life, low cutting force, and low energy consumption) was also determined to provide an insight on how energy consumption can be integrated with other machining responses towards sustainable machining process of metals

Multivariate change point estimation in covariance matrix using ANN

In statistical process control, change point estimation is an essential requirement for diagnosing the source of a deviation when a process is out of control. In this study, an ANN- based method is proposed to estimate the change point in the multivariate normal process which is subjected to covariance variation. Since in a physical system parameter is correlated, correlation is kept constant to obtain realistic simulated data. Employing statistical simulation, different out of control scenarios are simulated and statistics are calculated for each scenario. This study is to predict the change point in the control chart using the simulated set and corresponding statistical sets, an ANN is adopted. The resulting model achieved a high accuracy of 90% in training and 80% testing with a reasonable level of confidence in the prediction. Also, results show that Bayesian reaches a higher accuracy than Levenberg in ANN training

Optimization of surface roughness in deep hole drilling using moth-flame optimization

This study emphasizes on optimizing the value of machining parameters that will affect the value of surface roughness for the deep hole drilling process using moth-flame optimization algorithm. All experiments run on the basis of the design of experiment (DoE) which is two level factorial with four center point. Machining parameters involved are spindle speed, feed rate, depth of hole and minimum quantity lubricants (MQL) to obtain the minimum value for surface roughness. Results experiments are needed to go through the next process which is modeling to get objective function which will be inserted into the moth-flame optimization algorithm. The optimization results show that the moth-flame algorithm produced a minimum surface roughness value of 2.41μ compared to the experimental data. The value of machining parameters that lead to minimum value of surface roughness are 900 rpm of spindle speed, 50 mm/min of feed rate, 65 mm of depth of hole and 40 l/hr of MQL. The ANOVA has analysed that spindle speed, feed rate and MQL are significant parameters for surface roughness value with P-value <0.0001, 0.0219 and 0.0008 while depth of hole has P-value of 0.3522 which indicates that the parameter is not significant for surface roughness value. The analysis also shown that the machining parameter that has largest contribution to the surface roughness value is spindle speed with 65.54% while the smallest contribution is from depth of hole with 0.8%. As the conclusion, the application of artificial intelligence is very helpful in the industry for gaining good quality of products