Compacting solid waste materials generated in Missouri to form new products: final technical report

Presented at the 32nd Annual Conference of Missouri Waste ControlThe unique high-pressure compaction technology developed at Capsule Pipeline Research Center (CPRC) of University of Missouri-Columbia was used to study the compaction of combustible components of municipal solid waste and flyash generated from coal-fired power plants. By compaction, the combustible wastes can be turned into uniform, densified solids for use as fuel; the flyash can be turned into high-valued building elements such as bricks and blocks.This research project was sponsored by the Solid Waste Management Program, Missouri Department of Natural Resources (MDNR) for the period from January 1, 2001 to December 29, 2001. (MDNR Award Project no. 00038-1

A proposed EPRI tailored collaboration project

After five years of extensive R&D sponsored by government and industry, the coal log pipeline (CLP) technology for transportation of coal has been sufficiently developed through laboratory tests to warrant large-scale pre-commercial demonstration and testing. Meanwhile, a national survey of electric utilities and coal companies has produced fourteen potential CLP demonstration sites. A preliminary evaluation of the sites determined that at least seven of the fourteen sites are economically promising. The purpose of this EPRI-TC proposal is to conduct a large-scale pre-commercial test/demo of CLP to pave the way for commercial demonstration. Completion of this pre-commercial test/demo project in two years will enable construction of the first commercial CLP with minimum risk involved and with maximum success. The CLP technology involves compaction of coal into logs (large circular coal cylinders), and the transportation of such logs by an underground pipeline to the user--a power generation station. It is an innovative new coal pipeline system that can effectively compete with railroads and truck transportation. The economics of CLP has been thoroughly examined in 1995. It was found that the CLP is economically competitive with train for distances greater than about 100 miles, and competitive with truck for distances greater than about 30 miles. As compared to coal slurry pipeline, the CLP has the following advantages: (1) CLP transports twice as much coal than a coal slurry pipeline of the same diameter can. The cost of coal transportation by CLP is substantially lower than by slurry pipeline. (2) Dewatering coal logs is much simpler than dewatering slurry. (3) CLP can be restarted readily after lengthy shutdown. It has no restart problem. (4) CLP uses less energy than slurry pipeline for transporting the same amount of coal. (5) Coal log fuel is most versatile. Upon crushing it can be burned in any type of combustors--pulverized-coal, cyclone, fluidized-bed, or stoker. (6) Storage of coal logs is much simpler than storage of coal slurry. (7) CLP uses only 1/3 to 1/4 of the water used by slurry pipeline. This makes CLP more feasible than slurry pipeline in regions of water shortage. Development of the CLP technology will benefit electric utilities by reducing coal transportation cost--not only through use of CLP, but also due to the competition fostered which will cause rail rates and truck rates to keep within bounds. The pre-commercial test/demo project proposed herein contains four major components or tasks: (1) construction of a 6-inch-diameter, 3,000-ft-long coal pipeline for testing coal logs under conditions similar to those of future commercial CLP; (2) construction and testing of a coal log machine that can rapidly manufacture coal logs to supply coal log pipelines; (3) conducting a site-specific application study for each participating utilities; and (4) conducting an economic analysis of future commercial CLP systems using information gained in this study, and following EPRI cost guidelines. The project is for two years at a total cost of $825,960 of which$500,000 is requested from EPRI and participating utilities. As an EPRI Tailored Collaboration project, each participating utility is asked to contribute a total of $60,000 over two years (with equal matching from EPRI) to support this project. The targeted amount from utilities and EPRI for this project is$600,000 of which $100,000 is indirect cost to be used by EPRI to administer this project. This targeted amount can be achieved with five electric utilities participating. The project will also be cost-shared with$325,960 of the existing funds of the Capsule Pipeline Research Center (CPRC). Currently, the CPRC has insufficient funds to carry out this project without support from EPRI and some additional utility companies.Introduction -- Advantages of CLP -- Brief review of concept and state of development -- Project purpose and tasks -- Test facilities -- Statement of work -- Schedule of activities -- References -- Qualifications of institute and project personnel -- Budget -- Budget explanation -- Utilities participation and arrangements -- Intellectual property rights and patents -- Appendices. Document on preparing smooth welded joints for steel pipe ; CPRC's contract document (agreement) with existing industrial sponsors

Multi-fault diagnosis for rolling element bearings based on intrinsic mode function screening and optimized least squares support vector machine

Multi-fault diagnosis of rolling element bearing is significant to avoid serious accidents and huge economic losses effectively. However, due to the vibration signal with the character of nonstationarity and nonlinearity, the detection, extraction and classification of the fault feature turn into a challenging task. This paper presents a novel method based on redundant second generation wavelet packet transform (RSGWPT), ensemble empirical mode decomposition (EEMD) and optimized least squares support vector machine (LSSVM) for fault diagnosis of rolling element bearings. Firstly, this method implements an analysis combining RSGWPT-EEMD to extract the crucial characteristics from the measured signal to identify the running state of rolling element bearings, the vibration signal is adaptively decomposed into a number of modified intrinsic mode functions (modified IMFs) by two step screening processes based on the energy ratio; secondly, the matrix is formed by different level modified IMFs and singular value decomposition (SVD) is used to decompose the matrix to obtain singular value as eigenvector; finally, singular values are input to LSSVM optimized by particle swarm optimization (PSO) in the feature space to specify the fault type. The effectiveness of the proposed multi-fault diagnosis technique is demonstrated by applying it to both simulated signals and practical bearing vibration signals under different conditions. The results show that the proposed method is effective for the condition monitoring and fault diagnosis of rolling element bearings

Pin on Disc Wear volume Prediction Based on Grey System Theory

This paper is a study of pin on disc wear volume, with the MMW-1A vertical friction and wear testing machine as the testing equipment, under different lubrication conditions. In this paper, the pin wear volume GM(1,1) prediction model is built based on the grey system theory, GM(1,1) the model consists of a single variable in the first-order differential equation. The pin wear volume measured compare with GM(1,1) predicted wear volume, The comparison results showed that, the predicted values by the GM(1,1) are very close to the experiment measured values, and the precision of predicted results is quite high

Sparse decomposition based on ADMM dictionary learning for fault feature extraction of rolling element bearing

Sparse decomposition is a novel method for the fault diagnosis of rolling element bearing, whether the construction of dictionary model is good or not will directly affect the results of sparse decomposition. In order to effectively extract the fault characteristics of rolling element bearing, a sparse decomposition method based on the over-complete dictionary learning of alternating direction method of multipliers (ADMM) is presented in this paper. In the process of dictionary learning, ADMM is used to update the atoms of the dictionary. Compared with the K-SVD dictionary learning and non-learning dictionary method, the learned ADMM dictionary has a better structure and faster speed in the sparse decomposition. The ADMM dictionary learning method combined with the orthogonal matching pursuit (OMP) is used to implement the sparse decomposition of the vibration signal. The envelope spectrum technique is used to analyze the results of the sparse decomposition for the fault feature extraction of the rolling element bearing. The experimental results show that the ADMM dictionary learning method can updates the dictionary atoms to better fit the original signal data than K-SVD dictionary learning, the high frequency noise in the vibration signal of the rolling bearing can be effectively suppressed, and the fault characteristic frequency can be highlighted, which is very favorable for the fault diagnosis of the rolling element bearing

Fate of tetracycline and sulfonamide resistance genes in a grassland soil amended with different organic fertilizers

This study provided an assessment of the environmental fate of antibiotic resistance genes (ARGs) in a Scottish grassland field repeatedly treated with different organic fertilizers. The impacts of manure, biosolids and municipal food-derived compost on the relative abundances of tetracycline ARGs (tetA, tetB, tetC, tetG and tetW), sulfonamide ARGs (sul1 and sul2) and class 1 integron-integrase gene (IntI1) in soils were investigated, with inorganic fertilizer (NPK) as a comparison. The background soil with a history of low intensity farming showed a higher total relative abundance of tet ARGs over sul ARGs, with tetracycline efflux genes occurring in a higher frequency. In all treatments, the relative abundances of most ARGs detected in soils decreased over time, especially IntI1 and tet ARGs. This general attenuation of soil ARGs is a reflection of changes in the soil microbial community, which is supported by the result that almost all the soils at the end of the experiment had different bacterial communities from the untreated soil at the beginning of the experiment. Multiple applications of organic fertilizers to some extent counteracted the decreasing trend of soil ARGs relative abundances, which resulted in higher ARGs relative abundances in comparison to NPK, either by a lesser decrease of IntI1 and tet ARGs or an increase of sul ARGs. The enhancement of existing soil ARG prevalence by organic fertilizers was strongly dependent on the organic fertilizer type and the particular ARG. Compost contained the lowest relative abundance of inherent ARGs and had the least effect on the soil ARG decrease after application. The relative increase of tet ARGs caused by biosolids was larger than that of sul ARGs, while manure caused the opposite effect. Fertilization practices did not exert effective impacts on the soil bacterial community, although it caused significant changes in the profile of the ARG pool. Organic fertilization may thus accelerate the dissemination of ARGs in soil mainly through horizontal gene transfer (HGT), consistent with the enrichment of IntI1 in organic fertilized soils

Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth Evaluation

This paper presents a comprehensive evaluation of the Optical Character Recognition (OCR) capabilities of the recently released GPT-4V(ision), a Large Multimodal Model (LMM). We assess the model's performance across a range of OCR tasks, including scene text recognition, handwritten text recognition, handwritten mathematical expression recognition, table structure recognition, and information extraction from visually-rich document. The evaluation reveals that GPT-4V performs well in recognizing and understanding Latin contents, but struggles with multilingual scenarios and complex tasks. Specifically, it showed limitations when dealing with non-Latin languages and complex tasks such as handwriting mathematical expression recognition, table structure recognition, and end-to-end semantic entity recognition and pair extraction from document image. Based on these observations, we affirm the necessity and continued research value of specialized OCR models. In general, despite its versatility in handling diverse OCR tasks, GPT-4V does not outperform existing state-of-the-art OCR models. How to fully utilize pre-trained general-purpose LMMs such as GPT-4V for OCR downstream tasks remains an open problem. The study offers a critical reference for future research in OCR with LMMs. Evaluation pipeline and results are available at https://github.com/SCUT-DLVCLab/GPT-4V_OCR

Metagenomic insights into the abundance and composition of resistance genes in aquatic environments:Influence of stratification and geography

A global survey was performed with 122 aquatic metagenomic DNA datasets (92 lake water and 30 seawater) obtained from the Sequence Read Archive (SRA). Antibiotic resistance genes (ARGs) and metal resistance genes (MRGs) were derived from the dataset sequences via bioinformatic analysis. The relative abundances of ARGs and MRGs in lake samples were in the ranges ND (not detected)-1.34x10(0) and 1.22x10(-3) -1.98x10(-1) copies per 16S rRNA, which were higher than those in seawater samples. Among ARGs, multidrug resistance genes and bacitracin resistance genes had high relative abundances in both lake and sea water samples. Multimetal resistance genes, mercury resistance genes and copper resistance genes had the greatest relative abundance for MRGs. No significant difference was found between epilimnion and hypolimnion in abundance or the Shannon diversity index for ARGs and MRGs. Principal coordinates analysis and permutational multivariate analysis of variance (PERMANOVA) test showed that stratification and geography had significant influence on the composition of ARGs and MRGs in lakes (p < 0.05, PERMANOVA). Coastal seawater samples had significantly greater relative abundance and a higher Shannon index for both ARGs and MRGs than deep ocean and Antarctic seawater samples (p < 0.05, Kruskal-Wallis one-way ANOVA), suggesting that human activity may exert more selective pressure on ARGs and MRGs in coastal areas than those in deep ocean and Antarctic seawater