Energy Efficiency and Industry 4.0 in Wood Industry: A Review and Comparison to Other Industries

Haddouche, Mohamed; Ilinca, Adrian

doi:10.3390/en15072384

Cited by 6 publications

(3 citation statements)

References 98 publications

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“…Wood, a versatile natural resource, plays a dual role as both an energy generator when used as biomass and an energy consumer during processing activities, such as the machining of wood-based materials [ 1 ]. Drying, a crucial step in wood transformation, constitutes a significant portion (40% to 70%) of the total energy used in wood product manufacturing [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Thermo-Hydric Study of Wood-Based Materials under Thermal Comfort Conditions

Haddouche,

Martini,

Chaouch

et al. 2024

Materials

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This paper tackles the issue of moisture variation in wood-based materials, explicitly focusing on melamine-coated particleboard (hereafter referred to as melamine) and medium-density fiberboard (MDF) used in the third phase of wood industry transformation. The approach involves a comprehensive strategy for predicting moisture content variation, incorporating numerical simulation, experimental testing, and the application of artificial neural network (ANN) technology to enhance accuracy in furniture manufacturing. The developed ANN models are tailored to predict moisture content changes under specific thermal comfort conditions. Remarkably, these models demonstrate high precision, with an average error margin of only 1.40% for 8% moisture content (MC) and 2.85% for 12% MC in melamine, as well as 1.42% for 8% MC and 2.25% for 12% MC in MDF. These levels of precision surpass traditional models, emphasizing this study’s novelty and practical relevance to the industrial context. The findings indicate that ANN models adapt to diverse environmental conditions, presenting a robust tool for optimizing moisture management in wood-based materials. This research contributes valuable insights for improving the reliability and efficiency of moisture content predictions in the wood industry.

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Section: Introductionmentioning

confidence: 99%

Thermo-Hydric Study of Wood-Based Materials under Thermal Comfort Conditions

Haddouche,

Martini,

Chaouch

et al. 2024

Materials

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“…However, this approach has become more and more challenging to maintain in recent years. First off, a significant portion of China's wood processing industry still uses conventional, high-carbon fossil fuels [20]. Some places have a significant amount of outdated production capacity that has to be removed [21].…”

Section: Introductionmentioning

confidence: 99%

Carbon Footprint Assessment and Efficiency Measurement of Wood Processing Industry Based on Life Cycle Assessment

Zhang

Yang

2023

Sustainability

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The contradiction between economic development and the ecological barrier function of the wood processing sector is increasingly deepening. However, China has not yet established a life cycle database for the wood processing industry, which makes it difficult to scientifically evaluate its carbon emission efficiency. Therefore, based on the panel data of 31 provinces (autonomous regions and municipalities) in China from 2014 to 2020 published in the China Statistical Yearbook, this paper combined a life cycle assessment and dynamic network slacks-based model, and the carbon emission efficiency of different stages was measured from the multidimensional perspective of labor, energy, capital input, and economic and ecological benefits output. The results show that: (1) The overall carbon emission efficiency is 0.6161, and the coastal and northwest regions have higher efficiency; (2) The efficiency levels of Inner Mongolia, Shanghai, and another four provinces have been increasing while other regions have significant differences; (3) Beijing, Jiangsu, and the other four provinces are at an effective level overall. The most serious efficiency loss is at the processing stage. These findings provide an important reference for promoting the low-carbon cycle development of China’s wood processing industry and the formulation of policies in various regions.

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“…The studies by Haddouche and Ilinca (2022), Kanoun et al (2021), Maggiore et al (2021), Matsunaga et al (2022), Morelli et al (2022), Otoum et al (2022), Wachnik et al (2022) refer to IoT, Blockchain, robots, and AI as the most advanced and most common technologies of Industry 4.0 in the energy economy. The problem lies in the fact that despite the advantages of digital energetics disclosed in detail in the existing literature (Popkova, 2022), there is still much uncertainty as to whether these advantages are achieved in practice and how to maximize them.…”

Section: Introductionmentioning

confidence: 99%