Engineering Papers⌕ Search

DOE OSTI · 1107792

Integrated control system and method

Abstract

An integrated control system for use with an engine connected to a generator providing electrical power to a switchgear is disclosed. The engine receives gas produced by a gasifier. The control system includes an electronic controller associated with the gasifier, engine, generator, and switchgear. A gas flow sensor monitors a gas flow from the gasifier to the engine through an engine gas control valve and provides a gas flow signal to the electronic controller. A gas oversupply sensor monitors a gas oversupply from the gasifier and provides an oversupply signal indicative of gas not provided to the engine. A power output sensor monitors a power output of the switchgear and provide a power output signal. The electronic controller changes gas production of the gasifier and the power output rating of the switchgear based on the gas flow signal, the oversupply signal, and the power output signal.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Wang, Paul Sai Keat, Baldwin, Darryl, Kim, Myoungjin. 2013-10-29. Integrated control system and method. https://www.osti.gov/biblio/1107792

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗