Life-cycle energy and GHG emissions of forest biomass harvest and transport for biofuel production in Michigan

High dependence on imported oil has increased U.S. strategic vulnerability and prompted more research in the area of renewable energy production. Ethanol production from renewable woody biomass, which could be a substitute for gasoline, has seen increased interest. This study analysed energy use and greenhouse gas emission impacts on the forest biomass supply chain activities within the State of Michigan. A life-cycle assessment of harvesting and transportation stages was completed utilizing peer-reviewed literature. Results for forest-delivered ethanol were compared with those for petroleum gasoline using data specific to the U.S. The analysis from a woody biomass feedstock supply perspective uncovered that ethanol production is more environmentally friendly (about 62% less greenhouse gas emissions) compared with petroleum based fossil fuel production. Sensitivity analysis was conducted with key inputs associated with harvesting and transportation operations. The results showed that research focused on improving biomass recovery efficiency and truck fuel economy further reduced GHG emissions and energy consumption

Performance Analysis and Enhancement of Deep Convolutional Neural Network - Application to Gearbox Condition Monitoring

Convolutional neural network has been widely investigated for machinery condition monitoring, but its performance is highly affected by the learning of input signal representation and model structure. To address these issues, this paper presents a comprehensive deep convolutional neural network (DCNN) based condition monitoring framework to improve model performance. First, various signal representation techniques are investigated for better feature learning of the DCNN model by transforming the time series signal into different domains, such as the frequency domain, the time–frequency domain, and the reconstructed phase space. Next, the DCNN model is customized by taking into account the dimension of model, the depth of layers, and the convolutional kernel functions. The model parameters are then optimized by a mini-batch stochastic gradient descendent algorithm. Experimental studies on a gearbox test rig are utilized to evaluate the effectiveness of presented DCNN models, and the results show that the one-dimensional DCNN model with a frequency domain input outperforms the others in terms of fault classification accuracy and computational efficiency. Finally, the guidelines for choosing appropriate signal representation techniques and DCNN model structures are comprehensively discussed for machinery condition monitoring

Virtual sensing for gearbox condition monitoring based on extreme learning machine

Gearbox, as a critical component to convert speed and torque to maintain machinery normal operation in the industrial processes, has been received and still needs considerable attentions to ensure its reliable operation. Direct sensing and indirect sensing techniques are widely used for gearbox condition monitoring and fault diagnosis, but both have Pros and Cons. To bridge their gaps and enhance the performance of early fault diagnosis, this paper presents a new virtual sensing technique based on extreme learning machine (ELM) for gearbox degradation status estimation. By fusing the features extracted from indirect sensing measurements (e.g. in-process vibration measurement), ELM based virtual sensing model could infer the gearbox condition which was usually directly indicated by the direct sensing measurements (e.g. offline oil debris mass (ODM)). Different state-of-the-art dimension reduction techniques have been investigated for feature selection and fusion including principal component analysis (PCA) and its kernel version, locality preserving projection (LPP) method. The effectiveness of the presented virtual sensing technique is experimentally validated by the sensing measurements from a spiral bevel gear test rig. The experimental results show that the estimated gearbox condition by the virtual sensing model based on ELM and kernel PCA well follows the trend of truth data and presents the better performance over the support vector regression based virtual sensing scheme

Measuring the regional availability of forest biomass for biofuels and the potential of GHG reduction

Forest biomass is an important resource for producing bioenergy and reducing greenhouse gas (GHG) emissions. The State of Michigan in the United States (U.S.) is one region recognized for its high potential of supplying forest biomass; however, the long-term availability of timber harvests and the associated harvest residues from this area has not been fully explored. In this study time trend analyses was employed for long term timber assessment and developed mathematical models for harvest residue estimation, as well as the implications of use for ethanol. The GHG savings potential of ethanol over gasoline was also modeled. The methods were applied in Michigan under scenarios of different harvest solutions, harvest types, transportation distances, conversion technologies, and higher heating values over a 50-year period. Our results indicate that the study region has the potential to supply 0.75–1.4 Megatonnes (Mt) dry timber annually and less than 0.05 Mt of dry residue produced from these harvests. This amount of forest biomass could generate 0.15–1.01 Mt of ethanol, which contains 0.68–17.32 GJ of energy. The substitution of ethanol for gasoline as transportation fuel has potential to reduce emissions by 0.043–1.09 Mt CO2eq annually. The developed method is generalizable in other similar regions of different countries for bioenergy related analyses