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Article
Publication date: 13 October 2020

Russel Mhundwa and Michael Simon

This paper aims to show that a simplified surface fitting model can be efficient in determining the energy consumption during milk cooling by an on-farm direct expansion…

Abstract

Purpose

This paper aims to show that a simplified surface fitting model can be efficient in determining the energy consumption during milk cooling by an on-farm direct expansion bulk milk cooler (DXBMC). The study reveals that milk volume and the temperature gradient between the room and the final milk temperature can effectively be used for predicting the energy consumption within 95% confidence bounds.

Design/methodology/approach

A data acquisition system comprised a Landis and Gyr E650 power meter, TMC6-HE temperature sensors, and HOBO UX120-006M 4-channel analog data logger was designed and built for monitoring of the DXBMC. The room temperature where the DXBMC is housed was measured using a TMC6-HE temperature sensor, connected to a Hobo UX120-006M four-channel analog data logger which was configured to log at one-minute intervals. The electrical energy consumed by the DXBMC was measured using a Landis and Gyr E650 meter while the volume of milk was extracted from on the farm records.

Findings

The results showed that the developed model can predict the electrical energy consumption of the DXBMC within an acceptable accuracy since 80% of the variation in the electrical energy consumption by the DXBMC was explained by the mathematical model. Also, milk volume and the temperature gradient between the room and final milk temperature in the BMC are primary and secondary contributors, respectively, to electrical energy consumption by the DXBMC. Based on the system that has been monitored the findings reveal that the DXBMC was operating within the expected efficiency level as evidenced by the optimized electrical energy consumption (EEC) closely mirroring the modelled EEC with a determination coefficient of 0.95.

Research limitations/implications

Only one system was monitored due to unavailability of funding to deploy several data acquisition systems across the country. The milk blending temperatures, effects of the insulation of the DXBMC, were not taken into account in this study.

Practical implications

The developed model is simple to use, cost effective and can be applied in real-time on the dairy farm which will enable the farmer to quickly identify an increase in the cooling energy per unit of milk cooled.

Social implications

The developed easy to use model can be used by dairy farmers on similar on-farm DXBMC; hence, they can devise ways to manage their energy consumption on the farm during the cooling of milk and foster some energy efficiency initiatives.

Originality/value

The implementation of the developed model can be useful to dairy farmers in South Africa. Through energy optimization, the maintenance of the DXBMC can be determined and scheduled accordingly.

Details

Journal of Engineering, Design and Technology , vol. 19 no. 3
Type: Research Article
ISSN: 1726-0531

Keywords

Article
Publication date: 19 October 2015

Yashpal Patel, Aashish Kshattriya, Sarat B Singamneni and A. Roy Choudhury

Layered manufacturing with curved layers is a recently proposed rapid prototyping (RP) strategy for the manufacture of curved, thin and shell-type parts and the repair of…

Abstract

Purpose

Layered manufacturing with curved layers is a recently proposed rapid prototyping (RP) strategy for the manufacture of curved, thin and shell-type parts and the repair of worn surfaces, etc. The present investigation indicates another possible application area. In case of flat-layered RP of computer-aided design models having randomly located, small-dimensioned but critical surface features, adaptive slicing is resorted to. Large number of thin slices have to be employed to preserve the critical features. In contrast, a considerably lower number of curved thin slices would be required to preserve such surface features in case of RP with curved layers.

Design/methodology/approach

The method of preservation of critical features by RP with curved layers is formulated and demonstrated for two clusters of critical features on the surface of a part. A minimum number of such curved layers is identified by application of genetic algorithms (GAs) in case of a simple example. GA evolves the shape of the curved layer passing through the lower cluster so as to make a curved layer pass through the upper cluster of critical features.

Findings

In the example part, a 21 per cent reduction in the number of layers is achieved by the application of adaptive curved layers over adaptive straight layers.

Originality/value

The novelty of the concept is the proposed use of curved layered RP with adaptive slicing for the preservation of critical features in final prototyped part. This methodology, applied to part with two distinct clusters, leads to reduced number of layers compared to that obtained in flat-layered RP.

Details

Rapid Prototyping Journal, vol. 21 no. 6
Type: Research Article
ISSN: 1355-2546

Keywords

Article
Publication date: 5 June 2017

Stephen Loh Tangwe, Michael Simon and Edson Leroy Meyer

The purpose of this study was to build and develop mathematical models correlating ambient conditions and electrical energy to the coefficient of performance (COP) of an…

Abstract

Purpose

The purpose of this study was to build and develop mathematical models correlating ambient conditions and electrical energy to the coefficient of performance (COP) of an air-source heat pump (ASHP) water heater. This study also aimed to design a simulation application to compute the COP under different heating up scenarios, and to calculate the mean significant difference under the specified scenarios by using a statistical method.

Design/methodology/approach

A data acquisition system was designed with respect to the required sensors and data loggers on the basis of the experimental setup. The two critical scenarios (with hot water draws and without hot water draws) during the heating up cycles were analyzed. Both mathematical models and the simulation application were developed using the analyzed data.

Findings

The predictors showed a direct linear relationship to the COP under the no successive hot water draws scenario, while they exhibited a linear relationship with a negative gradient to the COP under the simultaneous draws scenario. Both scenarios showed the ambient conditions to be the primary factor, and the weight of importance of the contribution to the COP was five times more in the scenario of simultaneous hot water draws than in the other scenario. The average COP of the ASHP water heater was better during a heating cycle with simultaneous hot water draws but demonstrated no mean significant difference from the other scenario.

Research limitations/implications

There was a need to include other prediction parameters such as air speed, difference in condenser temperature and difference in compressor temperature, which could help improve model accuracy. However, these were excluded because of insufficient funding for the purchase of additional temperature sensors and an air speed transducer.

Practical implications

The research was conducted in a normal middle-income family home, and all the results were obtained from the collected data from the data acquisition system. Moreover, the experiment was very feasible because the conduction of the study did not interfere with the activities of the house, as occupants were able to carry out their activities as usual.

Social implications

This paper attempts to justify the system efficiency under different heating up scenarios. Based on the mathematical model, the performance of the system could be determined all year round and the payback period could be easily evaluated. Finally, from the study, homeowners could see the value of the efficiency of the technology, as they could easily compute its performance on the basis of the ambient conditions at their location.

Originality/value

This is the first research on the mathematical modeling of the COP of an ASHP water heater using ambient conditions and electrical energy as the predictors and by using surface fitting multi-linear regression. Further, the novelty is the design of the simulation application for a Simulink environment to compute the performance from real-time data.

Article
Publication date: 3 January 2017

Rachna Sehrawat, Paramjit S. Panesar, Tanya L. Swer and Anit Kumar

This paper aims to extract colour from micro-organisms (as a source of natural pigments) using agro-industrial substrates to replace synthetic media by solid state…

Abstract

Purpose

This paper aims to extract colour from micro-organisms (as a source of natural pigments) using agro-industrial substrates to replace synthetic media by solid state fermentation. Nature is filled with colours. Due to health and environmental consciousness among people, use of synthetic colour has declined, and so the need to develop colour from cheap and easily available natural sources (plants, animals, micro-organisms and algae) using a cost-effective technique with higher yield and rapid growth. Monascus purpureus colour is a potent source of compounds (Dimerumic acid, Monacolin-k and -aminobutyric acid) having antimutagenic, antimicrobial and antiobesity, which helps in combating diseases.

Design/methodology/approach

Response surface methodology was used to optimise the biopigments extraction from Monascus purpureus using solid state fermentation.

Findings

The best optimised conditions for biopigments production using Monascus purpureus MTCC 369 were pH 5.4 at 32°C for 8 days 9 hours (8.9 days) from sweet potato peel and pea pod powder, 7.8 (w/w) and 3.9 per cent (w/w), respectively, which gave a final yield of 21 CVU/g. The model F-value of 69.18 and high value of adjusted determination coefficient 96.00 per cent implies high level of significance of the fitted model.

Practical implications

Extracted colour can be used in beverages, confectionery and pharmaceutical industries.

Social implications

Colour produced using Monascus purpureus MTCC 369 is a natural source. As consumers are reluctant to use synthetic colour because of the undesirable allergic reactions caused by them, so a biopigment produced is natural colouring compound with wide application in food sector.

Originality/value

Selected sources of carbon and nitrogen were not used earlier by any researcher to extract biopigment from Monascus purpureus MTCC 369.

Details

Pigment & Resin Technology, vol. 46 no. 1
Type: Research Article
ISSN: 0369-9420

Keywords

Article
Publication date: 8 July 2020

M. Kaladhar

The present study spotlights the single and multicriteria decision-making (MCDM) methods to determine the optimal machining conditions and the predictive modeling for…

Abstract

Purpose

The present study spotlights the single and multicriteria decision-making (MCDM) methods to determine the optimal machining conditions and the predictive modeling for surface roughness (Ra) and cutting tool flank wear (VB) while hard turning of AISI 4340 steel (35 HRC) under dry environment.

Design/methodology/approach

In this study, Taguchi L16 design of experiments methodology was chosen. The experiments were performed under dry machining conditions using TiSiN-TiAlN nanolaminate PVD-coated cutting tool on which Taguchi and responses surface methodology (RSM) for single objective optimization and MCDM methods like the multi-objective optimization by ratio analysis (MOORA) were applied to attain optimal set of machining parameters. The predictive models for each response and multiresponse were developed using RSM-based regression analysis. S/N ratios, analysis of variance (ANOVA), Pareto diagram, Tukey's HSD test were carried out on experimental data for profound analysis.

Findings

Optimal set of machining parameters were obtained as cutting speed: at 180 m/min., feed rate: 0.05 mm/rev., and depth of cut: 0.15 mm; cutting speed: 145 m/min., feed rate: 0.20 mm/rev. and depth of cut: 0.1 mm for Ra and VB, respectively. ANOVA showed feed rate (96.97%) and cutting speed (58.9%) are dominant factors for Ra and VB, respectively. A remarkable improvement observed in Ra (64.05%) and VB (69.94%) after conducting confirmation tests. The results obtained through the MOORA method showed the optimal set of machining parameters (cutting speed = 180 m/min, feed rate = 0.15 mm/rev and depth of cut = 0.25 mm) for minimizing the Ra and VB.

Originality/value

This work contributes to realistic application for manufacturing industries those dealing with AISI 4340 steel of 35 HRC. The research contribution of present work including the predictive models will provide some useful guidelines in the field of manufacturing, in particular, manufacturing of gear shafts for power transmission, turbine shafts, fasteners, etc.

Details

Multidiscipline Modeling in Materials and Structures, vol. 17 no. 2
Type: Research Article
ISSN: 1573-6105

Keywords

Article
Publication date: 24 September 2021

Abhinav Kumar Sharma, Indrajit Mukherjee, Sasadhar Bera and Raghu Nandan Sengupta

The primary objective of this study is to propose a robust multiobjective solution search approach for a mean-variance multiple correlated quality characteristics…

Abstract

Purpose

The primary objective of this study is to propose a robust multiobjective solution search approach for a mean-variance multiple correlated quality characteristics optimisation problem, so-called “multiple response optimisation (MRO) problem”. The solution approach needs to consider response surface (RS) model parameter uncertainties, response uncertainties, process setting sensitivity and response correlation strength to derive the robust solutions iteratively.

Design/methodology/approach

This study adopts a new multiobjective solution search approach to determine robust solutions for a typical mean-variance MRO formulation. A fine-tuned, non-dominated sorting genetic algorithm-II (NSGA-II) is used to derive efficient multiobjective solutions for varied mean-variance MRO problems. The iterative search considers RS model uncertainties, process setting uncertainties and response correlation structure to derive efficient fronts. The final solutions are ranked based on two different multi-criteria decision-making (MCDM) techniques.

Findings

Five different mean-variance MRO cases are selected from the literature to verify the efficacy of the proposed solution approach. Results derived from the proposed solution approach are compared and contrasted with the best solution(s) derived from other approaches suggested in the literature. Comparative results indicate significant superiorities of the top-ranked predicted robust solutions in nondominated frequency, closeness-to-target and response variabilities.

Research limitations/implications

The solution approach depends on RS modelling and considers continuous search space.

Practical implications

In this study, promising robust solutions are expected to be more suitable for implementation than point estimate-based MOO solutions for a real-life MRO problem.

Originality/value

No evidence of earlier research demonstrates the superiority of a MOO-based iterative solution search approach for mean-variance MRO problems by simultaneously considering model uncertainties, response correlation and process setting sensitivity.

Details

International Journal of Quality & Reliability Management, vol. 39 no. 9
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 9 August 2011

William J. McCluskey and Richard A. Borst

The purpose of this paper is to describe a segmentation technique based on geostatistical modeling methods utilizing geographically weighted regression (GWR) to identify…

Abstract

Purpose

The purpose of this paper is to describe a segmentation technique based on geostatistical modeling methods utilizing geographically weighted regression (GWR) to identify submarkets which could be applied within the mass appraisal environment.

Design/methodology/approach

Given the spatial dimension within which neighbourhoods/submarkets exist, this paper has sought to utilize the geostatistical technique of GWR to identify them.

Findings

The efficacy of the procedure is established by demonstrating improvements in predictive accuracy of the resultant segmented market models as compared to a baseline global unsegmented model for each of the study areas. Optimal number of segments is obtained by measures of predictive accuracy, spatial autocorrelation in the residual errors and the Akaike information criterion.

Research limitations/implications

The three datasets used allowed for an evaluation of the robustness of the method. Nonetheless it would be beneficial to test it on other datasets, particularly from different regions of the world.

Practical implications

Many researchers and mass appraisal practitioners have established the benefit of segmenting a study area into two or more submarkets as a means of incorporating the effects of location within mass valuation models. This approach develops the existing knowledge.

Social implications

The research ultimately is developing more accurate valuation models upon which the property tax is based. This should create an environment of fair and acceptable assessed values by the tax paying community.

Originality/value

The contribution of this work lies in the methodological approach adopted which incorporates a market basket approach developed through a process of GWR. The importance of the research findings illustrate that submarket segmentation need no longer be an arbitrary process.

Details

International Journal of Housing Markets and Analysis, vol. 4 no. 3
Type: Research Article
ISSN: 1753-8270

Keywords

Article
Publication date: 20 March 2017

Sajad Pirsa and Fardin Mohammad Nejad

The purpose of this paper is to construct an array of sensors using polypyrrole–zinc oxide (PPy–ZnO) and PPy–vanadium (V; chemical formula: V2O5) fibers. To test responses…

Abstract

Purpose

The purpose of this paper is to construct an array of sensors using polypyrrole–zinc oxide (PPy–ZnO) and PPy–vanadium (V; chemical formula: V2O5) fibers. To test responses of sensors, a central composite design (CCD) has been used. The results of the CCD technique revealed that the developed sensors are orthogonally sensitive to diacetyl, lactic acid and acetic acid. In total, 20 different mixtures of diacetyl, lactic acid and acetic acid were prepared, and the responses of the array sensors were recorded for each mixture.

Design/methodology/approach

A response surface regression analysis has been used for correlating the responses of the sensors to diacetyl, lactic acid and acetic acid concentrations during the gas phase in food samples. The developed multivariate model was used for simultaneous determination of diacetyl, lactic acid and acetic acid concentrations. Some food samples with unknown concentrations of diacetyl, lactic acid and acetic acid were provided, and the responses of array sensors to each were recorded.

Findings

The responses of each sensor were considered as target response in a response optimizer, and by an overall composite desirability, the concentration of each analyte was predicted. The present work suggests the applicability of the response surface regression analysis as a modeling technique for correlating the responses of sensor arrays to concentration profiles of diacetyl, lactic acid and acetic acid in food samples.

Originality/value

The PPy–ZnO and PPy–V2O5 nanocomposite fibers were synthesized by chemical polymerization. The provided conducting fibers, PPy–ZnO and PPy–V2O5, were used in an array gas sensor system for the analysis of volatile compounds (diacetyl, lactic acid and acetic acid) added to yogurt and milk samples.

Details

Sensor Review, vol. 37 no. 2
Type: Research Article
ISSN: 0260-2288

Keywords

Article
Publication date: 1 June 2005

Wimalin Sukthomya and James D.T. Tannock

The paper describes the methods of manufacturing process optimization, using Taguchi experimental design methods with historical process data, collected during normal production.

2340

Abstract

Purpose

The paper describes the methods of manufacturing process optimization, using Taguchi experimental design methods with historical process data, collected during normal production.

Design/methodology/approach

The objectives are achieved with two separate techniques: the Retrospective Taguchi approach selects the designed experiment's data from a historical database, whilst in the Neural Network (NN) – Taguchi approach, this data is used to train a NN to estimate process response for the experimental settings. A case study illustrates both approaches, using real production data from an aerospace application.

Findings

Detailed results are presented. Both techniques identified the important factor settings to ensure the process was improved. The case study shows that these techniques can be used to gain process understanding and identify significant factors.

Research limitations/implications

The most significant limitation of these techniques relates to process data availability and quality. Current databases were not designed for process improvement, resulting in potential difficulties for the Taguchi experimentation; where available data does not explain all the variability in process outcomes.

Practical implications

Manufacturers may use these techniques to optimise processes, without expensive and time‐consuming experimentation.

Originality/value

The paper describes novel approaches to data acquisition associated with Taguchi experimentation.

Details

International Journal of Quality & Reliability Management, vol. 22 no. 5
Type: Research Article
ISSN: 0265-671X

Keywords

Content available
Article
Publication date: 1 December 1999

Nada Korac-Kakabadse

299

Abstract

Details

Journal of Managerial Psychology, vol. 14 no. 7/8
Type: Research Article
ISSN: 0268-3946

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