Logistic growth curve modeling of US energy production and consumption

Harris, Tyler M.; Devkota, Jay; Khanna, Vikas; Eranki, Pragnya L.; Landis, Amy E.

doi:10.1016/j.rser.2018.07.049

Cited by 47 publications

(28 citation statements)

References 40 publications

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“…In this direction, Harris et al (2018) developed a logistic growth curve model and identified that energy consumption in the United States until the year 2040 tends to grow more than the country's power generation, and it is necessary to continue importing fossil fuels (oil) due to increased demand forecasting. The key point well placed in the article is the need for investment, research and innovation to produce as much sustainable energy as possible to reduce carbon dioxide emissions.…”

Section: Independent Journal Of Management and Production (Ijmandp)mentioning

confidence: 99%

Control of the variability of the biofuel packaging process through the six sigma methodology: a case study

Júnior

Pianno

Achcar

2020

Ind. Jour. Manag. & Prod.

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The present work carried out the control of the variability in the biodiesel packaging through the application of the steps of the DMAIC (Define Measure, Analyze, Improve and Control) of the Six Sigma quality program. The DMAIC method brings together a set of statistical and quality tools for the study of process variability. Prior to the project, in the period evaluated between June and October of the year 2017 the average monthly variation of loss was 387.7 liters with a standard deviation of 421.9 liters. After the implementation of the improvements between January and March of the year 2018, the average monthly variation was -3.187 liters with a standard deviation of 60.95 liters. This evolution in the control and reduction of losses in the container of biodiesel was possible through the actions of improvements and involvement of the people of the billing and shipping. After all the improvements implemented, the maintenance and continuity of the controls met two important requirements of the Six Sigma quality program: customer focus and financial impact.

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Section: Independent Journal Of Management and Production (Ijmandp)mentioning

confidence: 99%

Control of the variability of the biofuel packaging process through the six sigma methodology: a case study

Júnior

Pianno

Achcar

2020

Ind. Jour. Manag. & Prod.

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“…The four-parameter multi-cycle logistic growth curve models were used in [32] to perform a case study of energy production and consumption in the US to 2040. In [33] it was shown that at present wind and solar power demonstrates early signs of logistic growth despite high learning rates and energy return on energy invested particularly in the PV sector.…”

Section: Technology Diffusionmentioning

confidence: 99%

“…It may be concluded, that the logistic growth models have been developed since the 19th century for a model population [35], in the 20th century as a modeling tool for fuel energy production [32]. In [36] the use of single logistic curve and logistic component analysis was presented focusing on the coherence between model, data and interpretation.…”

Section: Technology Diffusionmentioning

confidence: 99%

Long-Term Projection of Renewable Energy Technology Diffusion

2019

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The EU aims at increasing the use of renewable energy sources (RES), mainly solar-photovoltaic (PV) and wind technologies. Projecting the future, in this respect, requires a long-term energy modeling which includes a rate of diffusion of novel technologies into the market and the prediction of their costs. The aim of this article has been to project the pace at which RES technologies diffused in the past or may diffuse in the future across the power sector. This analysis of the dynamics of technologies historically as well as in modeling, roadmaps and scenarios consists in a consistent analysis of the main parameters of the dynamics (pace of diffusion and extent of diffusion in particular markets). Some scenarios (REMIND, WITCH, WEO, PRIMES) of the development of the selected power generation technologies in the EU till 2050 are compared. Depending on the data available, the learning curves describing the expected development of PV and wind technologies till 2100 have been modeled. The learning curves have been presented as a unit cost of the power versus cumulative installed capacity (market size). As the production capacity increases, the cost per unit is reduced thanks to learning how to streamline the manufacturing process. Complimentary to these learning curves, logistic S-shape functions have been used to describe technology diffusion. PV and wind generation technologies for the EU have been estimated in time domain till 2100. The doubts whether learning curves are a proper method of representing technological change due to various uncertainties have been discussed. A critical analysis of effects of the commonly applied models for a long-term energy projection (REMIND, WITCH) use has been conducted. It has been observed that for the EU the analyzed models, despite differences in the target saturation levels, predict stagnation in the development of PV and wind technologies from around 2040. Key results of the analysis are new insights into the plausibility of future deployment scenarios in different sectors, informed by the analysis of historical dynamics of technology diffusion, using to the extent possible consistent metrics.

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“…Finally, one can obtain the idea about the automation policy in a smart production system, where machinery systems are automatic and they are controlled by several automated system of energy (Dincer and Ezzat [48], Dincer and Al-Zareer [49], Dincer and Rosen [50], Dincer and Rosen [51], and Dincer and Bicer [52]). However, if the automated system is utilized in several research models (Lu et al [53], Kazemi et al [54], Kulczek [55], Harris et al [56], Bruni et al [57], Dehning et al [58], and Nordborg et al [59]), the optimum renewable energy under production maintenance is not considered. The main research gap regarding maintenance is not considered as it was assumed that smart production system always gives perfect smart products without any breakdown and without any maintenance ( Khalil et al [60], Keen et al [61], Chen et al [62], and Liang et al [63]).…”

Section: Introductionmentioning

confidence: 99%

Optimization of Safety Stock under Controllable Production Rate and Energy Consumption in an Automated Smart Production Management

Sarkar

2019

Energies

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A smart production system is essential to produce complex products under the consumption of efficient energy. The main ramification of controllable production rate, amount of production size, and safety stocks is simultaneously optimized under proper utilization of energy within a smart production system with a random breakdown of spare parts. Due to the random breakdown, a greater amount of energy may be used. For this purpose, this study is concerned about the optimum safety stock level under the exact amount of energy utilization. For random breakdown, there are three cases as production inventory meets the demand without utilization of the safety stock, with using of the safety stock, and consumed the total safety stock amount and facing shortages. After the random breakdown time, the smart production system may move to an out-of-control state and may produce defective items, where the production rate of defective items is a random variable, which follows an exponential distribution. The total cost is highly nonlinear and cannot be solved by any classical optimization technique. A mathematical optimization tool is utilized to test the model. Numerical study proves that the effect of energy plays an important role for the smart manufacturing system even though random breakdowns are there. it is found that the controllable production rate under the effect of the optimum energy consumption really effects significantly in the minimization cost. It saves cost regarding the corrective and preventive maintenance cost. The amount of safety stock can have more support under the effect of optimum energy utilization. The energy can be replaced by the solar energy.

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Logistic growth curve modeling of US energy production and consumption

Cited by 47 publications

References 40 publications

Control of the variability of the biofuel packaging process through the six sigma methodology: a case study

Control of the variability of the biofuel packaging process through the six sigma methodology: a case study

Long-Term Projection of Renewable Energy Technology Diffusion

Optimization of Safety Stock under Controllable Production Rate and Energy Consumption in an Automated Smart Production Management

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