Babcock & Wilcox Scores Coal Power Plant Retrofit Job in Southeast Asia

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Babcock & Wilcox Scores Coal Power Plant Retrofit Job in Southeast Asia

Akron, Ohio-headquartered Babcock & Wilcox Enterprises Inc. said its thermal business has been contracted to retrofit boiler cleaning equipment for a coal power plant in Southeast Asia. The company said in a media release that the contract is valued at around $13 million. B&W Thermal’s scope includes equipment design and supply, installation, integration and commissioning […]

B&W Thermal’s scope includes equipment design and supply, installation, integration and commissioning of its Diamond Power boiler cleaning equipment, including retractable and standard HydroJet boiler cleaning systems, soot blowers, and a Titanium advanced intelligent boiler cleaning control system, B&W said.

It added that the customer selected its advanced boiler cleaning technology to help the plant maintain stable, full-capacity power generation, improve operational efficiency, reduce coal consumption, and lower associated carbon emissions.

“B&W Thermal has a broad range of Diamond Power boiler cleaning technologies, cameras, monitoring equipment, replacement parts, soot blowing equipment, and more that can help plant owners improve the performance and longevity of their coal-fired power assets”, Chris Riker, B&W Executive Vice President and Chief Operating Officer, said. “B&W’s technologies are proven solutions for boiler cleaning, environmental and efficiency improvements and can be tailored to meet each plant’s needs based on fuel type and other factors”.

“Our dedicated Philippines Service Center team allows us to work closely with our customers throughout Southeast Asia”, Riker said. “We’ve successfully designed and installed similar boiler cleaning and soot blower upgrade solutions to customers throughout the region, and we’re excited about the opportunity to once again serve this important market”.

The project is currently underway, the company said, adding that it is anticipated to be completed in the first quarter of 2026.

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Scotland’s Mocean Energy targets Australia and US for expansion

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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