Behnam Hamedi and Alireza Mokhtar
The purpose of this study is to investigate and analysis of energy consumption for this industry. The core part of any energy management system (EnMS) in industry is to perfectly…
Abstract
Purpose
The purpose of this study is to investigate and analysis of energy consumption for this industry. The core part of any energy management system (EnMS) in industry is to perfectly monitor the energy consumption of significant users and to continuously improve the energy performance. In petrochemical plants, production deals with energy-intensive processes, and measuring energy performance for recognition and assessment of potentials for saving is critical.
Design/methodology/approach
The required data are exploited for the period of March 2011-August 2016 (data set: 2,012 days). Multivariate linear regression (MLR) and multi-layer perceptron artificial neural network (ANN) methods are separately used to anticipate the energy consumption. The baseline will be assumed as a reference to be compared with the actual data to estimate the real saving values. Finally, cumulative summations (CUSUM) are proposed and applied as an effective indicator for measurement of energy performance in an LDPE.
Findings
In this study, two statistical methods of MLR and ANN were used to design and develop a comprehensive energy baseline representing the predicted amounts of energy consumption based on the recognized drivers. Although both models imply robust outcomes, when the relative errors are taken into account, performance of ANN models appears fairly superior compared to the MLR model.
Originality/value
It is highly suggested to the ISO technical committee dealing with energy management standards, to consider the proposed model for baseline development in the future version of the standard ISO 50006 as the supplementary extension for the ISO 50001 for measuring energy performance using EnB and EnPI. As for future studies, the research can be extended to investigate the uncertainty and the model could also become completed applying more advanced ANNs such as recurrent neural networks.
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Maryam Doroodi and Alireza Mokhtar
The purpose of this paper is to predict the amount of energy consumption by using a suitable statistical method in some sectors and energy carriers, which has shown a significant…
Abstract
Purpose
The purpose of this paper is to predict the amount of energy consumption by using a suitable statistical method in some sectors and energy carriers, which has shown a significant correlation with greenhouse gas emissions.
Design/methodology/approach
After studying the correlation between energy consumption rates in different sectors of energy consumption and some energy carriers with greenhouse gas distribution (CO2, SO2, NOX and SPM), the most effective factors on pollution emission will be first identified and then predicted for the next 20 years (2015 to 2004). Furthermore, to determine the appropriate method for forecasting, two approaches titled “trend analysis” and “double exponential smoothing” will be applied on data, collected from 1967 to 2014, and their capabilities in anticipating will be compared to each other contributing MSD, MAD, MAPE indices and also the actual and projected time series comparison. After predicting the energy consumption in the sectors and energy carriers, the growth rate of consumption in the next 20 years is also calculated.
Findings
Correlation study shows that four energy sectors (industry sector, agriculture, transportation and household-general-commercial) and two energy carriers (electricity and natural gas) have shown remarkable correlation with greenhouse gas emissions. To predict the energy consumption in mentioned sectors and carriers, it is proven that double exponential smoothing method is more capable in predicting. The study shows that among the demand sectors, the industry will account for the highest consumption rate. Electricity will experience the highest rate among the energy careers. In fact, producing this amount of electricity causes emissions of greenhouse gases.
Research limitations/implications
Access to the data and categorized data was one of the main limitations.
Practical implications
By identifying the sectors and energy carriers that have the highest consumption growth rate in the next 20 years, it can be said that greenhouse gas emissions, which show remarkable correlation with these sectors and carriers, will also increase dramatically. So, their stricter control seems to be necessary. On the other hand, to control a particular greenhouse gas, it is possible to focus on the amount of energy consumed in the sectors and carriers that have a significant correlation with this pollutant. These results will lead to more targeted policies to reduce greenhouse gas emissions.
Social implications
The tendency of communities toward industrialization along with population growth will doubtlessly lead to more consumption of fossil fuels. An immediate aftermath of burning fuels is greenhouse gas emission resulting in destructive effects on the environment and ecosystems. Identifying the factors affecting the pollutants resulted from consumption of fossil fuels is significant in controlling the emissions.
Originality/value
Such analyses help policymakers make more informed and targeted decisions to reduce greenhouse gas emissions and make safer and more appropriate policies and investment.
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Mokhtar Aarabi, Alireza Salehi and Alireza Kashaninia
The purpose of this study is use to density functional theory (DFT) to investigate the molecular adsorption by PEDOT:PSS for different doping levels. DFT calculations are…
Abstract
Purpose
The purpose of this study is use to density functional theory (DFT) to investigate the molecular adsorption by PEDOT:PSS for different doping levels. DFT calculations are performed using the SIESTA code. In addition, the non-equilibrium Green’s function method is used within the TranSIESTA code to determine the quantum transport properties of molecular nanodevices.
Design/methodology/approach
Density functional theory (DFT) is used to investigate the molecular adsorption by PEDOT:PSS for different doping levels. DFT calculations are performed using the SIESTA code. In addition, the non-equilibrium Green’s function method is used within the TranSIESTA code to determine the quantum transport properties of molecular nanodevices.
Findings
Simulation results show very good sensitivity of Pd-doped PEDOT:PSS to ammonia, carbon dioxide and methane, so this structure cannot be used for simultaneous exposure to these gases. Silver-doped PEDOT:PSS structure provides a favorable sensitivity to ammonia in addition to exhibiting a better selectivity. If the experiment is repeated, the sensitivity is increased for a larger concentration of the applied gas. However, the sensitivity will decrease at a higher ratio than smaller concentrations of gas.
Originality/value
The advantages of the proposed sensor are its low-cost implementation and simple fabrication process compared to other sensors. Moreover, the proposed sensor exhibits appropriate sensitivity and repeatability at room temperature.
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Mitra Meijani, Alireza Rousta and Dariyoush Jamshidi
The expansion of lifestyle and luxury markets has necessitated new marketing techniques. Recently, brand addiction has been a new topic in luxury repurchasing. The information…
Abstract
Purpose
The expansion of lifestyle and luxury markets has necessitated new marketing techniques. Recently, brand addiction has been a new topic in luxury repurchasing. The information reported in the literature regarding the effectiveness of brand addiction is insufficient and controversial. This research aimed to assess the role of brand addiction in Islamic consumers who purchase luxury smartphone brands.
Design/methodology/approach
Survey responses were collected from an online sample of 384 luxury consumers in Iran. The methods were evaluated using software (smart PLS 3) to test the hypothesis.
Findings
According to the uniqueness theory, the authors completed that brand addiction and consumer relationships are different and relative in each luxury product. The results positively determine that brand addiction has a more significant impact than brand jealousy, brand love and brand experience in repurchasing luxury consumers.
Research limitations/implications
This study helps expand the literature on luxury repurchases and contends that brand addiction creates a new perspective in understanding behavioral addiction.
Practical implications
This paper provides insights for current and future marketers and managers, especially in Iran.
Originality/value
This investigation is the first study on the impact of different dimensions of brand addiction on luxury smartphone repurchase intention. In this regard, the findings of the study are important in the luxury market and extend current knowledge on repurchasing luxury products such as in Iran.
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Ning Li, Dai Liu and Francis Boadu
The construction of digital supply chains to integrate internal and external resources is becoming an important path for manufacturing enterprises to gain competitiveness…
Abstract
Purpose
The construction of digital supply chains to integrate internal and external resources is becoming an important path for manufacturing enterprises to gain competitiveness. However, at present, research on the internal mechanisms of digital supply chain capabilities (DSCC) and enterprise sustainable competitive performance (ESCP) has not been sufficiently studied. Based on contextual ambidexterity theory, this study investigates whether DSCC could enable the realization of supply chain ambidexterity and further explains the mediating role of supply chain ambidexterity on DSCC and ESCP, and the boundary conditions of supply chain governance on supply chain ambidexterity and ESCP.
Design/methodology/approach
With a survey data set of 232 Chinese manufacturing enterprises from different industries, the study empirically tests a moderated mediating model and conducts hierarchical linear modeling and bootstrap to test the study's hypotheses.
Findings
The results demonstrate that: (1) DSCC positively enhance ESCP; (2) supply chain ambidexterity, which can be regarded as a synergic ability of supply chain alignment and adaptability, partially mediates the positive relationship between DSCC and ESCP; and (3) supply chain governance such as incentive governance positively moderates the association between supply chain ambidexterity and ESCP, but there is no evidence that relational governance moderates their relationship.
Originality/value
This paper proposes a new interpretive perspective to understand digital supply chains. More importantly, it reveals the importance of DSCC in contributing toward supply chain ambidexterity and ESCP, and demonstrates the differential regulating action of incentive and relational governance on the association between supply chain ambidexterity and ESCP, with implications for both academics and practitioners.