York Resource Recovery Center Control System Upgrade

13th North American Waste-to-Energy Conference ◽

10.1115/nawtec13-3161 ◽

2005 ◽

Author(s):

Edward Salsbury

Keyword(s):

Control System ◽

Data Processing ◽

Solid Waste ◽

State Of The Art ◽

Resource Recovery ◽

Generation Process ◽

Family Control ◽

York County ◽

Westinghouse Electric Corporation ◽

Westinghouse Electric

The York Resource Recovery Center was constructed and completed for the York County Solid Waste and Refuse Authority by Westinghouse Electric Corporation in October 1989. The design incorporated three Westinghouse O’Connor Rotary Combustors fitted with Deltak Boilers to provide a state-of-the art incineration system for the 400,000 residents of York County. Westinghouse incorporated many of their industrial products into the plant including their Westinghouse Data Processing Family Control System (WDPF) for control of the plant combustion and generation process.

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WESTINGHOUSE B 66

Alloy Digest ◽

10.31399/asm.ad.cb0005 ◽

1963 ◽

Vol 12 (9) ◽

Keyword(s):

Physical Properties ◽

Tensile Properties ◽

Base Alloy ◽

Heat Treating ◽

Westinghouse Electric Corporation ◽

Westinghouse Electric

Abstract Westinghouse Alloy B66 is a columbium-base alloy exhibiting superior strength at temperatures in excess of 2000 F, yet retaining sufficient ductility for formability and weldability by conventional means. This datasheet provides information on composition, physical properties, elasticity, and tensile properties as well as creep. It also includes information on forming, heat treating, machining, and joining. Filing Code: Cb-5. Producer or source: Westinghouse Electric Corporation.

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K-42-B

Alloy Digest ◽

10.31399/asm.ad.ni0013 ◽

1954 ◽

Vol 3 (12) ◽

Keyword(s):

Elevated Temperatures ◽

Iron Alloy ◽

Heat Treating ◽

Cobalt Chromium ◽

Nickel Cobalt ◽

Creep Characteristics ◽

Westinghouse Electric Corporation ◽

Westinghouse Electric ◽

Hardening Heat Treatment ◽

Resistance To Heat

Abstract K-42-B is a nickel-cobalt-chromium-iron alloy having high resistance to heat and corrosion. It responds to a precipitation-hardening heat treatment producing high tensile and creep characteristics at elevated temperatures. This datasheet provides information on composition, physical properties, elasticity, and tensile properties as well as creep and fatigue. It also includes information on high temperature performance and corrosion resistance as well as heat treating, machining, and joining. Filing Code: Ni-13. Producer or source: Westinghouse Electric Corporation.

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NIVCO-10

Alloy Digest ◽

10.31399/asm.ad.co0037 ◽

1964 ◽

Vol 13 (6) ◽

Keyword(s):

Fracture Toughness ◽

Precipitation Hardening ◽

Turbine Blades ◽

High Strength ◽

Heat Treating ◽

Damping Capacity ◽

High Ductility ◽

Westinghouse Electric Corporation ◽

Westinghouse Electric ◽

Cobalt Base

Abstract Nivco 10 is a cobalt-base turbine alloy having a combination of high damping capacity, high strength and high ductility. It is a precipitation hardening alloy recommended for use at temperatures up to 1200 F, such as turbine blades. This datasheet provides information on composition, physical properties, elasticity, and tensile properties as well as fracture toughness, creep, and fatigue. It also includes information on forming, heat treating, machining, and joining. Filing Code: Co-37. Producer or source: Westinghouse Electric Corporation.

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BiLabel-Specific Features for Multi-Label Classification

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3458283 ◽

2021 ◽

Vol 16 (1) ◽

pp. 1-23

Author(s):

Min-Ling Zhang ◽

Jun-Peng Fang ◽

Yi-Bo Wang

Keyword(s):

Predictive Models ◽

Comparative Studies ◽

State Of The Art ◽

Classification Model ◽

Generation Process ◽

Prototype Selection ◽

Class Label ◽

Benchmark Datasets ◽

Label Correlations ◽

Class Labels

In multi-label classification, the task is to induce predictive models which can assign a set of relevant labels for the unseen instance. The strategy of label-specific features has been widely employed in learning from multi-label examples, where the classification model for predicting the relevancy of each class label is induced based on its tailored features rather than the original features. Existing approaches work by generating a group of tailored features for each class label independently, where label correlations are not fully considered in the label-specific features generation process. In this article, we extend existing strategy by proposing a simple yet effective approach based on BiLabel-specific features. Specifically, a group of tailored features is generated for a pair of class labels with heuristic prototype selection and embedding. Thereafter, predictions of classifiers induced by BiLabel-specific features are ensembled to determine the relevancy of each class label for unseen instance. To thoroughly evaluate the BiLabel-specific features strategy, extensive experiments are conducted over a total of 35 benchmark datasets. Comparative studies against state-of-the-art label-specific features techniques clearly validate the superiority of utilizing BiLabel-specific features to yield stronger generalization performance for multi-label classification.

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