Analyzing the Economics Values of An Alternative Preprocessing Facility in the Biomass Feedstocks - Biorefinery Supply Chain

It is generally believed that preprocessing procedure can reduce the transportation and storage costs of biomass feedstock for biofuel production by condensing the feedstock’s size. However, the capital costs of preprocessing facilities could be significant in the feedstock logistics system. Applying a GIS and mixed-integer mathematical programming model, this study evaluates the economic values of a preprocessing technology, stretch‐wrap baling, in the biomass feedstock supply chain for a potential commercial-scale switchgrass biorefinery in East Tennessee. Preliminary results suggest that the stretch-wrap baling equipment outperforms the conventional hay harvest methods in terms of total delivered costs. Although the densification process involves additional capital and operation costs, the total delivered costs of switchgrass for a 25- million-gallon per year biorefinery in the preprocessing system is 12% − 21% lower than various logistic methods using conventional hay equipments.Biomass feedstock, cellulosic biofuel, logistic costs, preprocessing technology, Crop Production/Industries, Resource /Energy Economics and Policy, Q16, D24,

Bearing Fault Diagnosis Method Based on EEMD and LSTM

The condition monitoring and fault detection of rolling bearing are of great significance to ensure the safe and reliable operation of rotating machinery system.In the past few years, deep neural network (DNN) has been recognized as an effective tool to detect rolling bearing faults. However, It is too complex to directly feed the original vibration signal to the DNN neural network, and the accuracy of fault identification is not high. By using the signal preprocessing technology, the original signal can be effectively removed and preprocessed without losing the key diagnosis information. In this paper, a new EEMD-LSTM bearing fault diagnosis method is proposed, which combines the signal preprocessing technology with the EEMD method that can get clear fault feature signals, and LSTM technology to extract fault features automatically that improves the efficiency of fault feature extraction. In the case of small sample size, this method can significantly improve the accuracy of fault diagnosis

Power System Parameters Forecasting Using Hilbert-Huang Transform and Machine Learning

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. Apart from introduction and references the paper is organized as follows. The section 2 presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learning-based algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting

Research on technical analysis of basketball match based on data mining

The aim of this paper is to preprocess basketball technology actions, to classify these actionswith data mining technology, to mine association rules among them. The main works are shown below:The common approaches of data mining are discussed, such as preprocessing technology, classification technology, clustering technology and mining rules technology. Both ID3 decision tree classification algorithm association and Apriori association rules algorithm are studied in detail.The paper discusses basketball technology actionsboth on a small scale and a large scale, J48 decision tree classification and Apriori association rules mining algorithm basketball are applied, all these research results should have useful instruction to team

LIFE CYCLE ASSESSMENT OF AIR CLASSIFICATION AS A SULFUR MITIGATION TECHNOLOGY IN PINE RESIDUE FEEDSTOCKS

Sulfur accumulation during biofuel production is pollutive, toxic to conversion catalysts, and causes the premature breakdown of processing equipment. Air classification is an effective preprocessing technology for ash and sulfur removal from biomass feedstocks. A life cycle assessment (LCA) sought to understand the environmental impacts of implementing air classification as a sulfur-mitigation technique for pine residues. Energy demand and material balance for preprocessing were simulated using SimaPro and the Argonne National Laboratory’s GREET model, specifically focusing on comparing the global warming potential (GWP) of grid electricity versus bioelectricity scenarios. Overall, the grid electricity scenario had a GWP impact over 7 times that of the bioelectricity scenario with the largest source of impact from steam generation during rotary drying. Air classification represents 0.4% and 1.6% of total GWP impact for the grid electricity and bioelectricity scenarios, respectively. Therefore, air classification can facilitate significant sulfur reduction to improve rates of biofuel conversion and lessen corrosion of combustion equipment while contributing minimal GWP impact during preprocessing

Strategic polymorphism requires just two combinators!

In previous work, we introduced the notion of functional strategies: first-class generic functions that can traverse terms of any type while mixing uniform and type-specific behaviour. Functional strategies transpose the notion of term rewriting strategies (with coverage of traversal) to the functional programming paradigm. Meanwhile, a number of Haskell-based models and combinator suites were proposed to support generic programming with functional strategies. In the present paper, we provide a compact and matured reconstruction of functional strategies. We capture strategic polymorphism by just two primitive combinators. This is done without commitment to a specific functional language. We analyse the design space for implementational models of functional strategies. For completeness, we also provide an operational reference model for implementing functional strategies (in Haskell). We demonstrate the generality of our approach by reconstructing representative fragments of the Strafunski library for functional strategies.Comment: A preliminary version of this paper was presented at IFL 2002, and included in the informal preproceedings of the worksho

Modeling of electricity demand forecast for power system

© 2019, Springer-Verlag London Ltd., part of Springer Nature. The emerging complex circumstances caused by economy, technology, and government policy and the requirement of low-carbon development of power grid lead to many challenges in the power system coordination and operation. However, the real-time scheduling of electricity generation needs accurate modeling of electricity demand forecasting for a range of lead times. In order to better capture the nonlinear and non-stationary characteristics and the seasonal cycles of future electricity demand data, a new concept of the integrated model is developed and successfully applied to research the forecast of electricity demand in this paper. The proposed model combines adaptive Fourier decomposition method, a new signal preprocessing technology, for extracting useful element from the original electricity demand series through filtering the noise factors. Considering the seasonal term existing in the decomposed series, it should be eliminated through the seasonal adjustment method, in which the seasonal indexes are calculated and should multiply the forecasts back to restore the final forecast. Besides, a newly proposed moth-flame optimization algorithm is used to ensure the suitable parameters of the least square support vector machine which can generate the forecasts. Finally, the case studies of Australia demonstrated the efficacy and feasibility of the proposed integrated model. Simultaneously, it can provide a better concept of modeling for electricity demand prediction over different forecasting horizons