A hierarchical physical model-based approach to predictive control of a thermal power plant for efficient plant-wide disturbance rejection

Extending the work presented in Prasad et al. (IEEE Proceedings on Control Theory and Applications, 147, 523-37, 2000), this paper reports a hierarchical nonlinear physical model-based control strategy to account for the problems arising due to complex dynamics of drum level and governor valve, and demonstrates its effectiveness in plant-wide disturbance handling. The strategy incorporates a two-level control structure consisting of lower-level conventional PI regulators and a higher-level nonlinear physical model predictive controller (NPMPC) for mainly set-point manoeuvring. The lower-level PI loops help stabilise the unstable drum-boiler dynamics and allow faster governor valve action for power and grid-frequency regulation. The higher-level NPMPC provides an optimal load demand (or set-point) transition by effective handling of plant-wide interactions and system disturbances. The strategy has been tested in a simulation of a 200-MW oil-fired power plant at Ballylumford in Northern Ireland. A novel approach is devized to test the disturbance rejection capability in severe operating conditions. Low frequency disturbances were created by making random changes in radiation heat flow on the boiler-side, while condenser vacuum was fluctuating in a random fashion on the turbine side. In order to simulate high-frequency disturbances, pulse-type load disturbances were made to strike at instants which are not an integral multiple of the NPMPC sampling period. Impressive results have been obtained during both types of system disturbances and extremely high rates of load changes, right across the operating range, These results compared favourably with those from a conventional state-space generalized predictive control (GPC) method designed under similar conditions.

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.

Spray Congealed Lipid Microparticles with High Protein Loading: Preparation and Solid State Characterization

The spray-congealing technique, a solvent-free drug encapsulation process, was successfully employed to obtain lipid-based particulate systems with high (10–20% w/w) protein loading. Bovine serum albumin (BSA) was utilised as model protein and three low melting lipids (glyceryl palmitostearate, trimirystin and tristearin) were employed as carriers. BSA-loaded lipid microparticles were characterised in terms of particle size, morphology and drug loading. The results showed that the microparticles exhibited a spherical shape, mean diameter in the range 150–300 µm and an encapsulation efficiency higher than 90%. Possible changes in the protein structure as a result of the manufacturing process was then investigated for the first time using UV spectrophotometry in fourth derivative mode and FT-Raman spectroscopy. The results suggested that the structural integrity of the protein was maintained within the particles. Thermal analysis indicated that the effect of protein on the thermal properties of the carriers could be detected. Spray-congealing could thus be considered a suitable technique to produce highly BSA-loaded microparticles preserving the structure of the protein.

Rapid succession of plant associations on the small ocean island of Mauritius at the onset of the Holocene

The island of Mauritius offers the opportunity to study the poorly understood vegetation response to climate change on a small tropical oceanic island. A high-resolution pollen record from a 10 m long peat core from Kanaka Crater (560 m elevation, Mauritius, Indian Ocean) shows that vegetation shifted from a stable open wet forest Last Glacial state to a stable closed-stratified-tall-forest Holocene state. An ecological threshold was crossed at ∼11.5 cal ka BP, propelling the forest ecosystem into an unstable period lasting ∼4000 years. The shift between the two steady states involves a cascade of four abrupt (<150 years) forest transitions in which different tree species dominated the vegetation for a quasi-stable period of respectively ∼1900, ∼1100 and ∼900 years. We interpret the first forest transition as climate-driven, reflecting the response of a small low topography oceanic island where significant spatial biome migration is impossible. The three subsequent forest transitions are not evidently linked to climate events, and are suggested to be driven by internal forest dynamics. The cascade of four consecutive events of species turnover occurred at a remarkably fast rate compared to changes during the preceding and following periods, and might therefore be considered as a composite tipping point in the ecosystem. We hypothesize that wet gallery forest, spatially and temporally stabilized by the drainage system, served as a long lasting reservoir of biodiversity and facilitated a rapid exchange of species with the montane forests to allow for a rapid cascade of plant associations.

State Estimation for Anaerobic Digesters using the ADM1

The optimization of full-scale biogas plant operation is of great importance to make biomass a competitive source of renewable energy. The implementation of innovative control and optimization algorithms, such as Nonlinear Model Predictive Control, requires an online estimation of operating states of biogas plants. This state estimation allows for optimal control and operating decisions according to the actual state of a plant. In this paper such a state estimator is developed using a calibrated simulation model of a full-scale biogas plant, which is based on the Anaerobic Digestion Model No.1. The use of advanced pattern recognition methods shows that model states can be predicted from basic online measurements such as biogas production, CH4 and CO2 content in the biogas, pH value and substrate feed volume of known substrates. The machine learning methods used are trained and evaluated using synthetic data created with the biogas plant model simulating over a wide range of possible plant operating regions. Results show that the operating state vector of the modelled anaerobic digestion process can be predicted with an overall accuracy of about 90%. This facilitates the application of state-based optimization and control algorithms on full-scale biogas plants and therefore fosters the production of eco-friendly energy from biomass.

Molecularly Imprinted Polymers for the isolation of bioactive naphthoquinones from plant extracts

Molecularly Imprinted Polymers (MIPs) targeting shikonin, a potent antioxidant and wound healing agent, have been prepared using methacrylic acid (MAA) and 2-diethylaminoethyl methacrylate (DEAEMA) as functional monomers. An investigation of solution association between shikonin and both acidic and basic functional monomers by UV-Vis titrations, suggested stronger affinity towards the basic functionality. Strong inhibition of the co-polymerisation reaction of such basic monomers was observed, but was overcome by reduction of the amount of template used during polymer synthesis. Polymer morphology was severely impacted by the template’s radical scavenging behaviour as demonstrated by solid state NMR spectroscopy measurements. HPLC evaluation of the final materials in polar conditions revealed limited imprinting effects and selectivity, with the MAA polymers exhibiting marginally better performance. During application of the polymers as MI-SPE sorbents in non-polar solvents it was found that the DEAEMA based polymer was more selective towards shikonin compared to the MAA counterpart, while shikonin recoveries of up to 72% were achieved from hexane solutions of a commercial sample of shikonin, hexane extract of Alkanna tinctoria roots and a commercial pharmaceutical ointment.

Application of PMUs to Coordinate Hydroelectric and Wind Power Plant Operation

It is acknowledged that wind power is a stochastic energy source compared to hydroelectric generation which is easily scheduled. In this paper a scheme for coordinating wind power plant and hydroelectric power plant is presented by using PMUs to measure and control the state of wind and hydro power plants. Hydroelectric generation is proposed as a method of energy reserve and compensation in the context of wind power fluctuation in order to avoid full or partial curtailment of wind generation to benefit wind providers. The feasibility of this proposed scheme is investigated by power flow calculation and stability analysis using the IEEE 30-bus power system model.