UK-China Agritech Challenge: Aquaculture 4.0 -- Advancing Digital Precision Aquaculture in China (ADPAC)

中英农业科技挑战赛：水产养殖 4.0——推进中国数字化精准水产养殖 (ADPAC)

• 批准号: BB/S020896/1
• 负责人: Tao Chen
• 金额: $ 58.26万
• 依托单位: University of Surrey
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FS020896%2F1
• 关键词: UK; China; Agritech; Challenge; Aquaculture

The project aims to advance digital precision aquaculture in China towards "Aquaculture 4.0", which is a highly connected and automated cyber-human-physical system using digital technologies. Aquaculture production in China reached 45.5 million tonnes in 2014 (60%+ of world's production, UN FAO). The grand challenge is the extremely low production efficiency, leading to high labour intensity, high energy/water consumption and environment pollution. Innovation is urgently needed for a transformation to digital industrialised aquaculture. The proposal is a timely response to the needs; it will apply and integrate the latest technologies of advanced sensors, 5G-based Internet of Things, Big Data analytics and automation to pilot highly digital precision aquaculture in China. The system is expected to deliver 10-fold increase in production efficiency. The project will open new business opportunities, gaining a competitive advantage by entering the Chinese market at this critical time and by partnering with the key innovators in Chinese aquaculture represented in this consortium. The solution will be also applicable to aquaculture worldwide opening up additional revenue stream.

该项目旨在推动中国的数字精准养殖向“水产养殖4.0”迈进，这是一个使用数字技术的高度连接和自动化的网络-人-物系统。2014年，中国水产养殖产量达到4550万吨（占世界产量的60%以上，联合国粮农组织）。最大的挑战是极低的生产效率，导致高劳动强度，高能耗/水消耗和环境污染。向数字化工业化水产养殖转型迫切需要创新。该提案是对需求的及时回应；它将应用和整合先进传感器、基于5g的物联网、大数据分析和自动化的最新技术，在中国试点高度数字化的精准养殖。该系统有望将生产效率提高10倍。该项目将开辟新的商业机会，通过在这个关键时刻进入中国市场，并与该财团中代表的中国水产养殖领域的主要创新者合作，获得竞争优势。该解决方案也将适用于全球水产养殖，开辟额外的收入来源。

项目成果

Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach

• DOI: 10.3390/app10207079
• 发表时间: 2020-10-01
• 期刊: APPLIED SCIENCES-BASEL
• 影响因子: 2.7
• 作者: Eze, Elias;Ajmal, Tahmina
• 通讯作者: Ajmal, Tahmina

Aquaculture 4.0: hybrid neural network multivariate water quality parameters forecasting model.

• DOI: 10.1038/s41598-023-41602-7
• 发表时间: 2023-09-26
• 期刊: Scientific reports
• 影响因子: 4.6

Developing a Novel Water Quality Prediction Model for a South African Aquaculture Farm

• DOI: 10.3390/w13131782
• 发表时间: 2021-07-01
• 期刊: WATER
• 影响因子: 3.4
• 作者: Eze, Elias;Halse, Sarah;Ajmal, Tahmina
• 通讯作者: Ajmal, Tahmina

A Lagrange Relaxation Based Decomposition Algorithm for Large-Scale Offshore Oil Production Planning Optimization

• DOI: 10.3390/pr9071257
• 发表时间: 2021-07
• 期刊: Processes
• 影响因子: 3.5
• 作者: Xiaoyong Gao;Yue Zhao;Yuhong Wang;Xin Zuo;Tao Chen

Aquaculture 4.0: Hybrid Neural Network Multivariate Water Quality Parameters Forecasting Model

水产养殖4.0：混合神经网络多元水质参数预测模型

• DOI: 10.21203/rs.3.rs-2711537/v1
• 发表时间: 2023
• 作者: Eze E