Integrated design and control using a dynamic inversely controlled process model

The profitability of chemical processes depends on their design and control. If the process design is fixed, there is little room left to improve control performance. Many commentators suggest design and control should be integrated. Nevertheless, the integrated problem is highly complex and intractable. This article proposes an optimization framework using a dynamic inversely controlled process model. The combinatorial complexities associated with the controllers are disentangled from the formulation, but the process and its control structure are still designed simultaneously. The new framework utilizes a multi-objective function to explore the trade-off between process and control objectives. The proposed optimization framework is demonstrated on a case study from the literature. Two parallel solving strategies are applied, and their implementations are explained. They are dynamic optimization based on (i) sequential integration and (ii) full discretization. The proposed integrated design and control optimization framework successfully captured the trade-off between control and process objectives

Implementation of a steady-state inversely controlled process model for integrated design and control of an ETBE reactive distillation

Recently, Sharifzadeh and Thornhill (2012) proposed a modeling approach for control structure selection using an inversely controlled process model, which benefits from significant complexity reductions. The treatment was based on the property that the inverse solution of a process model determines the best achievable control performance. The present article applies that methodology for integrated design and control of an ethyl tert-butyl ether (ETBE) reactive distillation column. In addition, the simulation-optimization program is reformulated using a penalty function, resulting in less optimization variables and better convergence of the simulation program. While the required computational efforts remain almost at the same level of steady-state process optimization, the process and its control structure are optimized simultaneously and regulatory steady-state operability of the solution is ensured. Finally, dynamic simulation is applied for detailed design of PI control loops

Carbon capture from natural gas combined cycle power plants: Solvent performance comparison at an industrial scale

Natural gas is an important source of energy. This article addresses the problem of integrating an existing natural gas combined cycle (NGCC) power plant with a carbon capture process using various solvents. The power plant and capture process have mutual interactions in terms of the flue gas flow rate and composition vs. the extracted steam required for solvent regeneration. Therefore, evaluating solvent performance at a single (nominal) operating point is not indicative and solvent performance should be considered subject to the overall process operability and over a wide range of operating conditions. In the present research, a novel optimization framework was developed in which design and operation of the capture process are optimized simultaneously and their interactions with the upstream power plant are fully captured. The developed framework was applied for solvent comparison which demonstrated that GCCmax, a newly developed solvent, features superior performances compared to the monoethanolamine baseline solvent

Current-Mode Techniques for the Implementation of Continuous- and Discrete-Time Cellular Neural Networks

This paper presents a unified, comprehensive approach to the design of continuous-time (CT) and discrete-time (DT) cellular neural networks (CNN) using CMOS current-mode analog techniques. The net input signals are currents instead of voltages as presented in previous approaches, thus avoiding the need for current-to-voltage dedicated interfaces in image processing tasks with photosensor devices. Outputs may be either currents or voltages. Cell design relies on exploitation of current mirror properties for the efficient implementation of both linear and nonlinear analog operators. These cells are simpler and easier to design than those found in previously reported CT and DT-CNN devices. Basic design issues are covered, together with discussions on the influence of nonidealities and advanced circuit design issues as well as design for manufacturability considerations associated with statistical analysis. Three prototypes have been designed for l.6-pm n-well CMOS technologies. One is discrete-time and can be reconfigured via local logic for noise removal, feature extraction (borders and edges), shadow detection, hole filling, and connected component detection (CCD) on a rectangular grid with unity neighborhood radius. The other two prototypes are continuous-time and fixed template: one for CCD and other for noise removal. Experimental results are given illustrating performance of these prototypes

Reducing MOSFET 1/f Noise and Power Consumption by "Switched Biasing"

Switched biasing is proposed as a technique for reducing the 1/f noise in MOSFET's. Conventional techniques, such as chopping or correlated double sampling, reduce the effect of 1/f noise in electronic circuits, whereas the switched biasing technique reduces the 1/f noise itself. Whereas noise reduction techniques generally lead to more power consumption, switched biasing can reduce the power consumption. It exploits an intriguing physical effect: cycling a MOS transistor from strong inversion to accumulation reduces its intrinsic 1/f noise. As the 1/f noise is reduced at its physical roots, high frequency circuits, in which 1/f noise is being upconverted, can also benefit. This is demonstrated by applying switched biasing in a 0.8 ¿m CMOS sawtooth oscillator. By periodically switching off the bias currents, during time intervals that they are not contributing to the circuit operation, a reduction of the 1/f noise induced phase noise by more than 8 dB is achieved, while the power consumption is also reduced by 30

A 0.18 μm CMOS low noise, highly linear continuous-time seventh-order elliptic low-pass filter

This paper presents a fast procedure for the system-level evaluation of noise and distortion in continuous-time integrated filters. The presented approach is based on Volterra's series theory and matrix algebra manipulation. This procedure has been integrated in a constrained optimization routine to improve the dynamic range of the filter while keeping the area and power consumption at a minimum. The proposed approach is demonstrated with the design, from system- to physical-level, of a seventh-order low-pass continuous-time elliptic filter for a high-performance broadband power-line communication receiver. The filter shows a nominal cut-off frequency of fc = 34MHz, less than 1dB ripple in the pass-band, and a maximum stop-band rejection of 65dB. Additionally, the filter features 12dB programmable boost in the pass-band to counteract high frequency components attenuation. Taking into account its wideband transfer characteristic, the filter has been implemented using G m-C techniques. The basic building block of its structure, the transconductor, uses a source degeneration topology with local feedback for linearity improving and shows a worst-case intermodulation distortion of -70 dB for two tones close to the passband edge, separated by 1MHz, with 70mV of amplitude. The filter combines very low noise (peak root spectral noise density below 56nV/√Hz) and high linearity (more than 64dB of MTPR for a DMT signal of 0.5Vpp amplitude) properties. The filter has been designed in a 0.18μm CMOS technology and it is compliant with industrial operation conditions (-40 to 85°C temperature variation and ±5% power supply deviation). The filter occupies 13mm2 and exhibits a typical power consumption of 450 mW from a 1.8V voltage supply.Ministerio de Ciencia y Tecnología TIC2003-0235

A framework for modelling, simulation and control of integrated urban wastewater system

This paper is concerned with the integrated modelling, and control of urban wastewater systems (UWS) comprising the wastewater treatment plants (WTP), receiving waters (river) and the sewer networks. A unified framework is developed and simple models are used and implemented in Matlab/Simulink to produce a toolbox. Novel linear and nonlinear control structures are then proposed to design integrated control systems to improve the river water quality. A case study is simulated and simulation results are presented to demonstrate the possible improvement that can be achieved using a holistic integrated control system approac

Matrix Methods for the Dynamic Range Optimization of Continuous-TimeGm-CFilters

This paper presents a synthesis procedure for the optimization of the dynamic range of continuous-time fully differential G m - C filters. Such procedure builds up on a general extended state-space system representation which provides simple matrix algebra mechanisms to evaluate the noise and distortion performances of filters, as well as, the effect of amplitude and impedance scaling operations. Using these methods, an analytical technique for the dynamic range optimization of weakly nonlinear G m - C filters under power dissipation constraints is presented. The procedure is first explained for general filter structures and then illustrated with a simple biquadratic section

Multi - objective sliding mode control of active magnetic bearing system

Active Magnetic Bearing (AMB) system is known to inherit many nonlinearity effects due to its rotor dynamic motion and the electromagnetic actuators which make the system highly nonlinear, coupled and open-loop unstable. The major nonlinearities that are associated with AMB system are gyroscopic effect, rotor mass imbalance and nonlinear electromagnetics in which the gyroscopics and imbalance are dependent to the rotational speed of the rotor. In order to provide satisfactory system performance for a wide range of system condition, active control is thus essential. The main concern of the thesis is the modeling of the nonlinear AMB system and synthesizing a robust control method based on Sliding Mode Control (SMC) technique such that the system can achieve robust performance under various system nonlinearities. The model of the AMB system is developed based on the integration of the rotor and electromagnetic dynamics which forms nonlinear time varying state equations that represent a reasonably close description of the actual system. Based on the known bound of the system parameters and state variables, the model is restructured to become a class of uncertain system by using a deterministic approach. In formulating the control algorithm to control the system, SMC theory is adapted which involves the formulation of the sliding surface and the control law such that the state trajectories are driven to the stable sliding manifold. The surface design involves the transformation of the system into a special canonical representation such that the sliding motion can be characterized by a convex representation of the desired system performances. Optimal Linear Quadratic (LQ) characteristics and regional pole-clustering of the closed-loop poles are designed to be the objectives to be fulfilled in the surface design where the formulation is represented as a set of Linear Matrix Inequality optimization problem. For the control law design, a new continuous SMC controller is proposed in which asymptotic convergence of the system’s state trajectories in finite time is guaranteed. This is achieved by adapting the equivalent control approach with the exponential decaying boundary layer technique. The newly designed sliding surface and control law form the complete Multi-objective SMC (MO-SMC) and the proposed algorithm is applied into the nonlinear AMB in which the results show that robust system performance is achieved for various system conditions. The findings also demonstrate that the MO-SMC gives better system response than the reported ideal SMC (I-SMC) and continuous SMC (C-SMC)

A bistable soft gripper with mechanically embedded sensing and actuation for fast closed-loop grasping

Soft robotic grippers are shown to be high effective for grasping unstructured objects with simple sensing and control strategies. However, they are still limited by their speed, sensing capabilities and actuation mechanism. Hence, their usage have been restricted in highly dynamic grasping tasks. This paper presents a soft robotic gripper with tunable bistable properties for sensor-less dynamic grasping. The bistable mechanism allows us to store arbitrarily large strain energy in the soft system which is then released upon contact. The mechanism also provides flexibility on the type of actuation mechanism as the grasping and sensing phase is completely passive. Theoretical background behind the mechanism is presented with finite element analysis to provide insights into design parameters. Finally, we experimentally demonstrate sensor-less dynamic grasping of an unknown object within 0.02 seconds, including the time to sense and actuate