Chapter A Framework for Learning System for Complex Industrial Processes

• Tác giả chính: Rahman, Moksadur (auth)
• Tác giả khác: Fentaye, Amare Desalegn (auth), Zaccaria, Valentina (auth), Aslanidou, Ioanna (auth), Dahlquist, Erik (auth), Kyprianidis, Konstantinos (auth)
• Định dạng: Điện tử Chương của sách
• Ngôn ngữ: Tiếng Anh
• Được phát hành: InTechOpen 2021
• Những chủ đề: Engineering: general; learning system, soft-sensors, model predictive control, fault detection, isolation and identification, information fusion
• Truy cập trực tuyến: OAPEN Library: download the publication; OAPEN Library: description of the publication

Tóm tắt

Due to the intense price-based global competition, rising operating cost, rapidly changing economic conditions and stringent environmental regulations, modern process and energy industries are confronting unprecedented challenges to maintain profitability. Therefore, improving the product quality and process efficiency while reducing the production cost and plant downtime are matters of utmost importance. These objectives are somewhat counteracting, and to satisfy them, optimal operation and control of the plant components are essential. Use of optimization not only improves the control and monitoring of assets, but also offers better coordination among different assets. Thus, it can lead to extensive savings in the energy and resource consumption, and consequently offer reduction in operational costs, by offering better control, diagnostics and decision support. This is one of the main driving forces behind developing new methods, tools and frameworks. In this chapter, a generic learning system architecture is presented that can be retrofitted to existing automation platforms of different industrial plants. The architecture offers flexibility and modularity, so that relevant functionalities can be selected for a specific plant on an as-needed basis. Various functionalities such as soft-sensors, outputs prediction, model adaptation, control optimization, anomaly detection, diagnostics and decision supports are discussed in detail.

số ISBN

intechopen.92899

Truy cập

Open Access