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Open Access
Book part
Publication date: 4 May 2018

Zulnazri and Sulhatun

Purpose – This purpose of the research is to investigate the process of manufacturing LDPE recycle thermoplastic composites with reinforcement oil palm empty fruit bunch (OPEFB…

Abstract

Purpose – This purpose of the research is to investigate the process of manufacturing LDPE recycle thermoplastic composites with reinforcement oil palm empty fruit bunch (OPEFB) biomass microfillers.

Design/Methodology/Approach – Methods of physical and chemical modification of OPEFB fibers into the LDPE matrix and the addition of some compatibilizer such as MAPE and xylene process through melt blending can improve mechanical properties, electrical properties, biodegradability, and improve the morphology of composites.

Research Limitations/Implications – These composites are prepared by the following matrix ratio: filler (70:30)% and filler size (63, 75, 90, and 106) μm. The LDPE plastic is crushed to a size of 0.5–1 cm, then pressed with hot press free heating for 5 min and with a pressure of 10 min at 145 °C. Based on the characterization obtained, the tensile strength and the high impact on the use of 106 μm filler is 13.86 MPa and 3,542.6 J/m2, and thermal stability indicates the degradation temperature (T0) 497.83 °C. FT-IR analysis shows the presence of functional groups of cellulose and lignin molecules derived from TKKS collected in the composite.

Practical Implications – Based on the characterization obtained, this composite can be applied as furniture material and vehicle dashboard.

Originality/Value – Composites obtained from recycle of LDPPE plastics waste has some advantages such as good compatibility and high tensile strength. This composite used the OPEFB filler whose size is in micrometer, and so this product is different from other products.

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Proceedings of MICoMS 2017
Type: Book
ISBN:

Keywords

Book part
Publication date: 13 April 2023

David Philippov and Tomonobu Senjyu

In scientific works on forecasting price volatility (of which the overwhelming majority, in comparison with works on price forecasting) for energy products: crude oil, natural…

Abstract

In scientific works on forecasting price volatility (of which the overwhelming majority, in comparison with works on price forecasting) for energy products: crude oil, natural gas, fuel oil, the authors compared the effectiveness of forecasting models of generalized autoregressive heteroscedasticity (Generalized Autoregressive Conditional Heteroscedastic model, GARCH) with regression of support vectors for futures contracts. GARCH models are a standard tool used in the literature on volatility, and the vector machine nonlinear regression model is one of the machine learning methods that has been gaining huge popularity in recent years. The authors have shown that the accuracy of volatility forecasts for energy and aluminum prices significantly depends on the volatility proxy used. The model with correctly defined parameters can lead to fewer prediction errors than GARCH models when the square of the daily yield is used as an indicator of volatility in the evaluation. In addition, it is difficult to choose the best model among GARCH models, but forecasts based on asymmetric GARCH models are often the most accurate. The work is based on a model with a representative investor who solves the problem of optimizing utility in a two-period model. The key assumption of the model is the homogeneity of energy and aluminum investor preferences, that is, preferences do not change over time. There are also works with an attempt to solve this problem in a continuous state space. A completely new theory has been put forward that allows predicting the movement of the underlying asset without using historical data, so this topic is very relevant.

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Renewable Energy Investments for Sustainable Business Projects
Type: Book
ISBN: 978-1-80382-884-8

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Book part
Publication date: 27 June 2014

Andrew H. Chen, James A. Conover and John W. Kensinger

Analysis of Information Options offers new tools for evaluating investments in research, mineral exploration, logistics, energy transmission, and other information operations…

Abstract

Analysis of Information Options offers new tools for evaluating investments in research, mineral exploration, logistics, energy transmission, and other information operations. With Information Options, the underlying assets are information assets and the rules governing exercise are based on the realities of the information realm (infosphere). Information Options can be modeled as options to “purchase” information assets by paying the cost of the information operations involved. Information Options arise at several stages of value creation. The initial stage involves observation of physical phenomena with accompanying data capture. The next refinement is to organize the data into structured databases. Then bits of information are selected from storage and synthesized into an information product (such as a management report). Next, the information product is presented to the user via an efficient interface that does not require the user to be a field expert. Information Options are similar in concept to real options but substantially different in their details, since real options have physical objects as the underlying assets and the rules governing exercise are based on the realities of the physical world. Also, while exercising a financial option typically kills the option, Information Options may include multiple exercises. Information Options may involve high volatility or jump processes as well, further enhancing their value. This chapter extends several important real option applications into the information realm, including jump process models and models for valuing options to synthesize any of n information items into any of m output assets.

Book part
Publication date: 4 September 2003

Stan Aungst, Russell R. Barton and David T. Wilson

Quality Function Deployment (QFD) proposes to take into account the “voice of the customer,” through a list of customer needs, which are (qualitatively) mapped to technical…

Abstract

Quality Function Deployment (QFD) proposes to take into account the “voice of the customer,” through a list of customer needs, which are (qualitatively) mapped to technical requirements in House One. But customers do not perceive products in this space, nor do they not make purchase decisions in this space. Marketing specialists use statistical models to map between a simpler space of customer perceptions and the long and detailed list of needs. For automobiles, for example, the main axes in perceptual space might be categories such as luxury, performance, sport, and utility. A product’s position on these few axes determines the detailed customer requirements consistent with the automobiles’ position such as interior volume, gauges and accessories, seating type, fuel economy, door height, horsepower, interior noise level, seating capacity, paint colors, trim, and so forth. Statistical models such as factor analysis and principal components analysis are used to describe the mapping between these spaces, which we call House Zero.

This paper focus on House One. Two important steps of the product development process using House One are: (1) setting technical targets; (2) identifying the inherent tradeoffs in a design including a position of merit. Utility functions are used to determine feature preferences for a product. Conjoint analysis is used to capture the product preference and potential market share. Linear interpolation and the slope point formula are used to determine other points of customer needs. This research draws from the formal mapping concepts developed by Nam Suh and the qualitative maps of quality function deployment, to present unified information and mapping paradigm for concurrent product/process design. This approach is the virtual integrated design method that is tested upon data from a business design problem.

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Evaluating Marketing Actions and Outcomes
Type: Book
ISBN: 978-0-76231-046-3

Abstract

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Harnessing the Power of Failure: Using Storytelling and Systems Engineering to Enhance Organizational Learning
Type: Book
ISBN: 978-1-78754-199-3

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