| 1. |
- Sharma, Amit Kumar, et al.
(författare)
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Environment-Friendly Biodiesel/Diesel Blends for Improving the Exhaust Emission and Engine Performance to Reduce the Pollutants Emitted from Transportation Fleets
- 2020
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Ingår i: International Journal of Environmental Research and Public Health. - : MDPI. - 1661-7827 .- 1660-4601. ; 17:11
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Tidskriftsartikel (refereegranskat)abstract
- Biodiesel derived from biomass is a renewable source of fuel, and global application of biodiesel in the transport sector has rapidly expanded over the last decade. However, effort has been made to overcome its main shortcoming, i.e., efficiency and exhaust emission characteristics (NOx emissions) in unmodified diesel engines. Biodiesel combustion generally results in lower unburned hydrocarbons (HC), carbon monoxide (CO), and particulate matter (PM) in exhaust emissions compared to fossil diesel. In this study, various biodiesel blends (Chlorella vulgaris, Jatropha curcus, and Calophyllum inophyllum) were investigated for fuel characteristics, and engine performance with exhaust emission compared to diesel. Chlorella vulgaris, Jatropha curcus, and Calophyllum inophyllum biodiesel were synthesized by the acid–base transesterification approach in a microwave reactor and blended with conventional diesel fuel by volume. The fuel blends were denoted as MB10 (90% diesel + 10% microalgae biodiesel), MB20 (80% diesel + 20% microalgae biodiesel), JB10 (90% diesel + 10% jatropha biodiesel), JB20 (80% diesel + 20% jatropha biodiesel), PB10 (90% diesel + 10% polanga biodiesel) and PB20 (80% diesel + 20% polanga biodiesel). Experiments were performed using these fuel blends with a single-cylinder four-stroke diesel engine at different loads. It was shown in the results that, at rated load, thermal efficiency of the engine decreased from 34.6% with diesel to 34.1%, 33.7%, 34.1%, 34.0%, 33.9%, and 33.5% with MB10, MB20, JB10, JB20, PB10, and PB20 fuels, respectively. Unburned hydrocarbon, carbon monoxide and smoke emissions improved with third-generation fuels (MB10, MB20) in comparison to base diesel fuel and second-generation fuels (JB10, JB20, PB10 and PB20). Oxides of nitrogen emissions were slightly increased with both the third- and second-generation fuels as compared to the base diesel. The combustion behavior of microalgae biodiesel was also very close to diesel fuels. In the context of comparable engine performance, emissions, and combustion characteristics, along with biofuel production yield (per year per acre), microalgae biodiesel could have a great potential as a next-generation sustainable fuel in compression engine (CI) engines compared to jatropha and polanga biodiesel fuels.
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| 2. |
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| 3. |
- Baglee, David, et al.
(författare)
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How can SMEs adopt a new method to advanced maintenance strategies : A Case study approach
- 2018
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Ingår i: Conference Proceedings: 30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017). - : University of Central Lancashire. - 9781909755154 ; , s. 155-162
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Konferensbidrag (refereegranskat)abstract
- Maintenance is crucial to manufacturing operations. In many organisations, the production equipmentrepresents the majority of invested capital, and deterioration of these facilities and equipment increasesproduction costs, reduces product quality. Over recent years the importance of maintenance, and thereforemaintenance management, within manufacturing organisations has grown. The maintenance function hasbecome an increasingly important and complex activity, particularly as automation increases. Theopportunity exists for many organisations to benefit substantially through improvements to theircompetitiveness and profitability by adopting a new approach to maintenance management. Several toolsand technologies including Condition Based Maintenance (CBM), Reliability Centred Maintenance (RCM)and more recently e-maintenance have developed under the heading of Advanced Maintenance Strategies.However, the adoption of advanced maintenance strategies and their potential benefits are usuallydemonstrated in large organisations. Unfortunately, the majority of organisations are constrained by thelack of knowledge and understanding on the requirements, which need to be in place before adopting anadvanced maintenance strategy. These are usually classified as Small and Medium Sized Enterprises(SMEs).The research strategy is based on ‘empirical iterations’ using survey secondary data, experts’ interviewsinformation and multiple case studies. The results show that there is a set of recommendations, whichstrongly influence the implementation of an Advanced Maintenance Strategy (AMS) with a Small toMedium Enterprise (SME). Organisations require a structured and integrative approach in order to takeadvantage of a new approach to maintenance management. This paper will propose recommendations forintegrating an AMS into the organisation and provide evidence of a successful implementation.
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| 4. |
- Soam, Shveta, 1987-, et al.
(författare)
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Life cycle assessment of rice straw utilization practices in India
- 2017
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Ingår i: Bioresource Technology. - : Elsevier BV. - 0960-8524 .- 1873-2976. ; 228, s. 89-98
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Tidskriftsartikel (refereegranskat)abstract
- The aim of this study is to find potential utilization practice of rice straw in India from an environmental perspective. Life cycle assessment (LCA) is conducted for four most realistic utilization practices of straw including: (1) incorporation into the field as fertilizer (2) animal fodder (3) electricity (4) biogas. The results show that processing of 1 ton straw to electricity and biogas resulted in net reduction of 1471 and 1023 kg CO2 eq., 15.0 and 3.4 kg SO2 eq. and 6.7 and 7.1 kg C2H6 eq. emissions in global warming, acidification and photochemical oxidation creation potential respectively. Electricity production from straw replaces the coal based electricity and resulted in benefits in most of the environmental impacts whereas use as an animal fodder resulted in eutrophication benefits. The burning of straw is a harmful practice of managing straw in India which can be avoided by utilizing straw for bioenergy.
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| 5. |
- Campos, Jaime, et al.
(författare)
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Business performance measurements in asset management with the support of big data technologies
- 2016
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Ingår i: Proceedings of MPMM 2016. - : Luleå tekniska universitet. - 9789175838410 ; , s. 89-95
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Konferensbidrag (refereegranskat)abstract
- The paper reviews the performancemeasurement in the domain of interest. Important data in assetmanagement are further, discussed. The importance and thecharacteristics of today’s ICTs capabilities are also mentionedin the paper. The role of new concepts such as big data anddata mining analytical technologies in managing theperformance measurements in asset management are discussedin detail. The authors consequently suggest the use of themodified Balanced Scorecard methodology highlighting bothquantitative and qualitative aspects, which is crucial foroptimal use of the big data approach and technologies.
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| 6. |
- Baglee, David, et al.
(författare)
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Optimizing Condition Monitoring of Big Data Systems
- 2017
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Ingår i: Proceedings of the 2017 International Conference on Data Mining. - : CSREA Press. - 1601324537 ; , s. 127-131
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Konferensbidrag (refereegranskat)abstract
- Industrial communication networks are common in a number of manufacturing organisations. The high availability of these networks is crucial for smooth plant operations. Therefore local and remote diagnostics of these networks is of primary importance in determining issues relating to plant reliability and availability. Condition Monitoring (CM) techniques when connected to a network provide a diagnostic system for remote monitoring of manufacturing equipment. The system monitors the health of the network and the equipment and is therefore able to predict performance. However, this leads to the collection, storage and analyses of large amounts of data, which must provide value. These large data sets are commonly referred to as Big Data. This paper presents a general concept of the use of condition monitoring and big data systems to show how they complement each other to provide valuable data to enhance manufacturing competiveness.
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| 7. |
- Bishnoi, Sunita, et al.
(författare)
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Electrochemical Sensing of Chlorpyrifos, a Carcinogen Responsible for Breast Cancer, in Milk and Plasma of Lactating Mothers
- 2023
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Ingår i: Electroanalysis. - : Wiley. - 1040-0397 .- 1521-4109. ; 35:2
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Tidskriftsartikel (refereegranskat)abstract
- In view of the increase in breast cancer cases at the global level, electrochemical sensing of the carcinogenic pesticide, chlorpyrifos (CPF) in breast milk is proposed. The determination is based on the nucleophilic substitution reaction of pralidoxime (PAM) with CPF. The proposed method offers a linear concentration range of 0.002 to 0.08 μmol/L. The limit of detection and limit of quantification was found to be 0.05×10−9 and 0.167×10−9M, respectively. The offered “unmodified edge plane pyrolytic graphite sensor” proved to be a better substrate than the earlier reported modified sensors. The limit of detection for the proposed method was found to be nearly fifty times lower than reported at modified electrodes. The interference study proved the adequate selectivity of the offered sensor. The sensor has good stability and reproducibility along with high sensitivity. The offered sensor is very useful for cancer hospitals, pesticide industries, and the study of environmental toxicity-related issues.
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| 8. |
- Campos, Jaime, et al.
(författare)
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Business Performance Measurements in Asset Management with the Support of Big Data Technologies
- 2017
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Ingår i: Management Systems in Production Engineering. - : De Gruyter Open. - 2299-0461 .- 2450-5781. ; 25:3, s. 143-149
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Tidskriftsartikel (refereegranskat)abstract
- The paper reviews the performance measurement in the domain of interest. Important data in asset management are further, discussed. The importance and the characteristics of today’s ICTs capabilities are also mentioned in the paper. The role of new concepts such as big data and data mining analytical technologies in managing the performance measurements in asset management are discussed in detail. The authors consequently suggest the use of the modified Balanced Scorecard methodology highlighting both quantitative and qualitative aspects, which is crucial for optimal use of the big data approach and technologies.
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| 9. |
- Heddam, Salim, et al.
(författare)
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Hybrid river stage forecasting based on machine learning with empirical mode decomposition
- 2024
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Ingår i: Applied water science. - : Springer Nature. - 2190-5487 .- 2190-5495. ; 14:3
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Tidskriftsartikel (refereegranskat)abstract
- The river stage is certainly an important indicator of how the water level fluctuates overtime. Continuous control of the water stage can help build an early warning indicator of floods along rivers and streams. Hence, forecasting river stages up to several days in advance is very important and constitutes a challenging task. Over the past few decades, the use of machine learning paradigm to investigate complex hydrological systems has gained significant importance, and forecasting river stage is one of the promising areas of investigations. Traditional in situ measurements, which are sometime restricted by the existing of several handicaps especially in terms of regular access to any points alongside the streams and rivers, can be overpassed by the use of modeling approaches. For more accurate forecasting of river stages, we suggest a new modeling framework based on machine learning. A hybrid forecasting approach was developed by combining machine learning techniques, namely random forest regression (RFR), bootstrap aggregating (Bagging), adaptive boosting (AdaBoost), and artificial neural network (ANN), with empirical mode decomposition (EMD) to provide a robust forecasting model. The singles models were first applied using only the river stage data without preprocessing, and in the following step, the data were decomposed into several intrinsic mode functions (IMF), which were then used as new input variables. According to the obtained results, the proposed models showed improved results compared to the standard RFR without EMD for which, the error performances metrics were drastically reduced, and the correlation index was increased remarkably and great changes in models’ performances have taken place. The RFR_EMD, Bagging_EMD, and AdaBoost_EMD were less accurate than the ANN_EMD model, which had higher R≈0.974, NSE≈0.949, RMSE≈0.330 and MAE≈0.175 values. While the RFR_EMD and the Bagging_EMD were relatively equal and exhibited the same accuracies higher than the AdaBoost_EMD, the superiority of the ANN_EMD was obvious. The proposed model shows the potential for combining signal decomposition with machine learning, which can serve as a basis for new insights into river stage forecasting.
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| 10. |
- Jantunen, Erkki, et al.
(författare)
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Digitalisation of Maintenance
- 2017
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Ingår i: 2nd International Conference on System Reliability and Safety, ICSRS 2017, 20 - 22 December 2017, Milan, Italy. - : IEEE. - 9781538633229 ; , s. 343-347
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Konferensbidrag (refereegranskat)abstract
- The digitalisation of industry and the Industry 4.0 concept with its connected ICTs are important developments for the industry to acquire and implement to be able to keep ahead in competition. In connection to Industry 4.0, the predictive maintenance systems and approach are getting more popular in maintenance. This is because these systems enable a change in the maintenance mind-set where the break-fix mentality is substituted by a predictive maintenance system, such as Condition Based Maintenance (CBM), where the equipment is monitored with the support of ICTs to detect failures before they occur. The Industry 4.0 seems more attainable even for small and medium sized companies because of the drop in the prices of the components of these systems, both in the sensing elements and in the data processing part. The manufacturing methods used in the Integrated Circuit (IC) industry create the possibility to reduce significantly the price tag of sensors and processors. Therefore, the authors go through the CBM approach and technologies, such as Microelectromechanical System (MEMS) sensors as well as such emerging ICTs as the Cloud and Big data. These could offer a turning point in traditional maintenance by widening the amount of monitored assets, allowing multiple parameters to be measured and analysed and enabling wireless and immediate data access across the globe.
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