Helen Shang

Microalgae cultivation in a novel top-lit gas-lift open bioreactor.

This work investigated a top-lit open microalgae bioreactor that uses a gas-lift system to enable deeper production depths, thereby significantly reducing the footprint. Growth of Scenedesmus sp. in a one-meter deep system by sparged with 6% CO2-enhanced air was evaluated. The results gave comparable volumetric biomass productivity (0.06 g(dw) L(-1) day(-1)), but around three-times higher areal productivity (60.0 g(dw)m(-)(2) day(-)(1)) than reported for traditional raceways. The lipid content of the Scenedesmus sp. was increased by 27% with an enhanced level of CO2 in the sparging gas.

Subspace identification with prior steady-state information

In system identification, the quality of data is important for obtaining good models, but there are situations where the available data are highly corrupted with noise. However, some prior information about the system to be identified, such as dc gain and settling time, may be available to obtain improved model identification despite data noise. In this paper, a subspace identification scheme incorporating known dc gain is investigated. The prior process information is incorporated into system identification through using the least square with the equality constraint that the sum of impulse response parameters is equal to the dc gain. In comparison with the existing approaches, the proposed identification strategy provides unbiased parameter estimations, is applicable to Multi-Input Multi-Output (MIMO) systems, and is computationally efficient.

Observer based fault detection for two dimensional systems described by Roesser models

Fault detection and isolation for two dimensional (2-D) systems represent a great challenge in both theoretical development and applications. 2-D systems have been commonly represented by the Roesser Model and the Fornasini and Marchesini (F–M) model. Research on fault detection and isolation has been carried out using observer-based methods for the F–M model. In this paper, an observer based fault detection strategy is investigated for systems modelled by the Roesser model. Using the 2-D polynomial matrix technique, a dead-beat observer is developed and the state estimate from the observer is then input to a residual generator to monitor occurrence of faults. An enhanced realization technique is combined to achieve efficient fault detection with reduced computations. Simulation results indicate that the proposed method is effective in detecting faults for systems without disturbances as well as those affected by unknown disturbances.

Optimal Operation of an Industrial PVC Dryer

This article describes the design of a control structure architecture for an industrial polyvinyl chloride (PVC) dryer, currently operating at Braskem Company (Marechal Deodoro, Alagoas, Brazil). The underlying motivation is to search for a control configuration that leads to optimal economic operation, while promptly rejecting disturbances at lower layers in the control hierarchy. We start by optimizing a nonlinear model of the process with respect to reducing energy consumption as a criterion of optimization for different disturbance scenarios. The results show that it is optimal to control the temperature level of the utilities serving the dryer and the outlet PVC moisture contents at their respective upper bounds. In addition, the flow of air to the dryer should be fixed at its optimum nominal setpoint despite disturbances. Application of this strategy results in a reduction of about 16% in energy consumption with respect to the current dryer operation policy and a 22% increase in throughput under nominal operation. In addition, almost perfect indirect control of the outlet PVC moisture was achieved by tightly controlling a temperature difference in the dryer. The proposed decentralized control configuration gives good dynamic performance for the outlet PVC moisture content with maximum settling time of about 1.8 h for the more difficult disturbance of increasing the inlet slurry moisture content by 40% and magnitude of overshoot of ca. 5% w.r.t. the optimum setpoint for an increase of 20% in PVC feed flow rate to the dryer.

Recursive subspace identification with prior information using the constrained least squares approach

Abstract It is essential to develop high quality models for process control and other applications. The incorporation of prior information in subspace identification has been investigated to obtain improved model quality. One of the recent developments incorporates the prior information using the constrained least squares (CLS). In many online applications, the amount of process data for model identification grows with time, and it is therefore necessary to develop a recursive algorithm for online identification of process models and to address the time-varying characteristics of the systems. In this paper, a recursive subspace identification algorithm incorporating prior information is developed using the constrained recursive least squares (CRLS). It is shown via a simulation example that the state space model identified using the proposed algorithm possesses improved accuracy.

Hill Valley Function Based Niching Particle Swarm Optimization for Multimodal Functions

A novel niching Particle Swarm Optimization (PSO) method based on a hill valley function is proposed. In this algorithm, the hill valley function is used to decide whether the niching seed particle and its neighbour are on the same hill, and if they are, a new niching is formed. The hill valley function is also used to decide whether two niching subswarms are on the same hill, and if they are, the two niching subswarms are merged. The proposed algorithm is evaluated using three benchmark test functions. Results indicate that the proposed hill valley function based niching PSO algorithm has strong adaptive searching capability and efficient convergence in searching multiple solutions.

A subspace algorithm for identifying 2-D CRSD systems with deterministic inputs

In this paper, the class of subspace system identification algorithms is used to derive a new identification algorithm for 2-D causal, recursive, and separable-in-denominator (CRSD) state space systems in the Roesser model form. The algorithm take a given deterministic input-output pair of 2-D signals and computes the system order (n) and system parameter matrices {A;B;C;D}. Since the CRSD model can be treated as two 1-D systems, the proposed algorithm first separates the vertical component from the state and output equations and then formulates an equivalent set of 1-D horizontal subspace equations. The solution to the horizontal subspace identification subproblem contains all the information necessary to compute the system order and parameter matrices, including those from the vertical subsystem.