Plant-wide predictive control for a thermal power plant based on a physical plant model

A constrained non-linear, physical model-based, predictive control (NPMPC) strategy is developed for improved plant-wide control of a thermal power plant. The strategy makes use of successive linearisation and recursive state estimation using extended Kalman filtering to obtain a linear state-space model. The linear model and a quadratic programming routine are used to design a constrained long-range predictive controller One special feature is the careful selection of a specific set of plant model parameters for online estimation, to account for time-varying system characteristics resulting from major system disturbances and ageing. These parameters act as nonstationary stochastic states and help to provide sufficient degrees-of-freedom to obtain unbiased estimates of controlled outputs. A 14th order non-linear plant model, simulating the dominant characteristics of a 200 MW oil-fired pou er plant has been used to test the NPMPC algorithm. The control strategy gives impressive simulation results, during large system disturbances and extremely high rate of load changes, right across the operating range. These results compare favourably to those obtained with the state-space GPC method designed under similar conditions.

COD removal from textile industry effluent: pilot plant studies

This work involved the treatment of industrial waste water from a nylon carpet printing plant. As dyeing of nylon is particularly difficult, acid dyes, fixing agents, thickeners, finishing agents, are required for successful colouration and cause major problems with the plant's effluent disposal in terms of chemical oxygen demand (COD). Granular activated carbon (GAC) Filtrasorb 400 was used to treat a simulated process plant effluent containing all the pollutants. Equilibrium isotherm experiments were established and experimental data obtained showed good empirical correlation with Langmuir isotherm theory. Column experimental data, in terms of COD were correlated using the bed depth service time (BDST) model. Solid phase loading in the columns were found to approach that in equilibrium studies indicating an efficient use of adsorbent. The results from the BDST model were then used to design a pilot adsorption rig at the plant. The performance of the pilot plant column were accurately predicted by scale-up from the bench scale columns. (C) 2001 Elsevier Science BN. All rights reserved.

The cost of water from an autonomous wave-powered desalination plant

A techno-economic model of an autonomous wave-powered desalination plant is developed and indicates that fresh water can be produced for as little as £0.45/m3. The advantages of an autonomous wave-powered desalination plant are also discussed indicating that the real value of the system is enhanced due to its flexibility for deployment and reduced environmental impact. The modelled plant consists of the Oyster wave energy converter, conventional reverse osmosis membranes and a pressure exchanger–intensifier for energy recovery. A time-domain model of the plant is produced using wave-tank experimentation to calibrate the model of Oyster, manufacturer's data for the model of the reverse osmosis membranes and a hydraulic model of the pressure exchanger–intensifier. The economic model of the plant uses best-estimate cost data which are reduced to annualised costs to facilitate the calculation of the cost of water. Finally, the barriers to the deployment of this technology are discussed, but they are not considered insurmountable.