A deep-sea salmon farming facility near Qingdao

https://jp.news.cn/20250116/9dc88fa0753f468da2fc4772c53548ec/c.html?page=1

In a large-scale deep sea smart fishery farming facility “Deep Blue 2” in the Qingdao National Deep Sea and Ocean Green Aquaculture Test Area, approximately 400,000 salmon are farmed in farming cages, with a high survival rate and healthy growth.

According to Gu Qihuan, production manager at Shandong Caijing Wanzefeng Marine Technology Co., Ltd., salmon farming requires strict environmental conditions, and it is very difficult to find suitable sea areas for large-scale farming. However, this location is home to 130,000 square kilometers of Yellow Sea cold water mass, and the water temperature in summer is 10 to 16 degrees Celsius, which is very suitable for salmon growth. Deep Blue 2 sinks to the level of the cold water mass less than 30 meters in summer and rises to the surface again in winter.

Deep Blue 2 is 71.5 meters high, 70 meters in diameter, and has a fully submerged farming area of ​​90,000 cubic meters. It is equipped with multiple smart farming equipment such as an automatic feeding system and an underwater photography system, making unmanned farming in the deep sea and distant ocean possible.

For harvest, schools of salmon are sucked up one after another onto work boats, passed through a fish-water separator, and transported to a workshop for processing. Workers place the salmon in insulated boxes filled with ice and transport them to land overnight for processing and sale.

The freshly caught salmon weigh an average of 3-4 kilograms each, and each harvest is about 5,000 fish. At the earliest, they can be delivered to major cities in China in just over 30 hours.

more insights

http://english.cas.cn/newsroom/research-news/202604/t20260423_1157877.shtml

https://doi.org/10.1016/j.tibtech.2026.03.017

A team led by the CAS Qingdao Institute of Bioenergy and Bioprocess Technology (QIBEBT) has developed a new “process ramanomics” platform. This technology enables real-time, data-driven control of biomanufacturing.

The researchers validated their approach in polyhydroxyalkanoate (PHA) fermentation, a key route for biodegradable polyesters used in packaging and medical materials. Powered by machine learning, the platform achieved 99.75% accuracy in distinguishing PHB-producing cells from P34HB-producing ones, and quantified total PHA content and monomer composition at the single-cell level with a median absolute deviation below 3.8%, comparable to traditional gas chromatography.

In a pivotal 5,000-liter industrial fermenter trial, traditional offline testing pointed to harvesting at 28 hours when PHA content registered 66.32%. Process ramanomics, however, revealed a compositional shift invisible to conventional methods: the 4HB monomer ratio was 8.67% at 26 hours (within specification) but climbed to 11.28% by 28 hours, exceeding the compliance limit, demonstrating that earlier termination could safeguard product quality.

The platform’s single-cell resolution also showed that the content of intracellular PHA can vary by more than threefold among individual cells. At 26 hours, population heterogeneity was lowest, with 91.54% of cells producing at high levels and a 4HB composition that was within specification. This confirmed that 26 hours was the optimal harvest window.

The scientists further showed that process ramanomics can be applied to different chassis organisms and products. For example, it can be used for protein synthesis in yeast and lipid synthesis in Rhodococcus. This suggests that process ramanomics could serve as a general-purpose analytics engine for next-generation intelligent bioreactors.

https://en.people.cn/n3/2026/0324/c90000-20439477.html

At the Qingdao Humanoid Robot Data Training Center in Laoshan District of Qingdao, humanoid robots are trained for jobs such as intelligent industrial manufacturing, smart home, and commercial services. Data collectors here control robots to complete specific tasks like logistics sorting, supermarket restocking, kitchen operations, and component assembly. Through thousands of repetitions and trials, massive amounts of motion data are generated, endowing robots with a smarter “intelligent brain,” and helping humanoid robots enter all walks of life to serve thousands of households.

https://www.cas.cn/syky/202602/t20260226_5102870.shtml

https://doi.org/10.1186/s40168-026-02339-3

A research team at the CAS Qingdao Institute of Bioenergy and Bioprocess Technology has developed RamEx, an integrated analysis framework for Ramanome big data. This platform, tailored to the characteristics of Raman spectroscopy data, establishes a one-stop workflow from data reading and standardized preprocessing to downstream data mining, centered on automated quality control algorithms and efficient parallel computing processes. It also demonstrates a systematic analysis of microbial metabolomical heterogeneity and metabolic pattern differentiation at the single-cell level.

Raman genomics deep analysis can track the dynamic changes in the composition of macromolecules such as lipids, proteins, and nucleic acids in different cells, thus revealing the differentiation and succession patterns of microbial metabolic states at the population scale with single-cell precision. This provides new research ideas and technical pathways for understanding the functional organization and environmental adaptation mechanisms of complex communities.

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