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Abstract: This paper presents a new system for the optimized control of IKN-type cement coolers based on adaptive predictive methodology. The new system introduces the concepts of ’layer on grate’ and ’thickness of slope’, which allow a decoupled multivariable control of the layer of clinker over the grate, and the flow rate of the cooler fans, solving a fundamental control problem for this type of process and maximizing the heat transfer from the cooling process to the kiln. The experimental results obtained in the Gador mill of Holcim, in Almeria (Spain), are analyzed. [Copyright &y& Elsevier]

Abstract: This paper presents a signal analysis methodology to validate (detect) and reconstruct the missing and false data of a large set of flow meters in the telecontrol system of a water distribution network. The proposed methodology is based on two time-scale forecasting models: a daily model based on a ARIMA time series, while the 10-min model is based on distributing the daily flow using a 10-min demand pattern. The demand patterns have been determined using two methods: correlation analysis and an unsupervised fuzzy logic classification, named LAMDA algorithm. Finally, the proposed methodology has been applied to the Barcelona water distribution network, providing very good results. [Copyright &y& Elsevier]

The 2002 IFAC Congress in Barcelona (B’02) was the first Congress in the new millennium, and thus it offered an unique opportunity for IFAC Technical Committees to plan for their future. Each Coordinating Committee within IFAC was asked to prepare a milestone report that summarized past accomplishments and future challenges. The milestone report was organized around 6 questions that are given below in the manuscript. For Area 7, industrial applications, the future growth areas that were identified and which are discussed in the paper are: information technology, environmental applications, new sensor technologies, genomics, and mechatronics. [Copyright &y& Elsevier]

Fault detection based on data from the supervisory control and data acquisition (SCADA) system, which has been installed in most MW-scale wind turbines, has brought significant benefits for wind farm operators. However, the changes in the features of hardware sensor measurements, which are used in current SCADA systems, often cannot provide reliable early alarms. In order to resolve this problem, in this paper, a novel dynamic model sensor method is proposed for the SCADA data based wind turbine fault detection. A dynamic model representing the relationship between the generator temperature, wind speed, and ambient temperature is derived following the first principles and used as the basic structure of the model sensor. When the model sensor is applied for fault detection, its parameters are updated regularly using the generator temperature, wind speed, and ambient temperature data from the SCADA system. Then, from the updated model, the fault sensitive features of wind turbine system are extracted via performing system frequency analysis and used for the turbine fault detection. This novel model sensor method is applied to the SCADA data of a wind farm of 3 wind turbines currently operating in Spain. The results show that the proposed method can not only detect the turbine generator fault but also reveal the trend of ageing with the wind turbine generator, demonstrating its capability of failure prognosis for wind turbine system and components. • A novel dynamic model sensor method is proposed for the SCADA data-based wind turbine fault detection. • A dynamic model of the generator temperature is derived following the first principles. • A novel nonlinear system frequency analysis known as NOFRFs approach is adopted to extract damage sensitive features. • The proposed method achieves excellent results on detecting the turbine generator failure and revealing the winding ageing trend. [ABSTRACT FROM AUTHOR]