Dead-Time Compensation for the First-Order Dead-Time Processes: Towards a Broader Overview

The article reviews the results of a number of recent papers dealing with the revision of the simplest approaches to the control of first-order time-delayed systems. The concise introductory review is extended by an analysis of two discrete-time approaches to dead-time compensation control of stable, integrating, and unstable first-order dead-time processes including simple diagnostics of the model used and focusing on the possibility of simplified but reliable plant modelling. The first approach, based on the first historically known dead-time compensator (DTC) with possible dead-beat performance, is based on the reconstruction of the actual process variables and the compensation of input disturbances by an extended state observer (ESO). Such solutions play an important role both in a disturbance observer (DOB) based control and in an active disturbance rejection control (ADRC). The second approach considered comes from the Smith predictor with two degrees of freedom, which combines feedforward control with output disturbance reconstruction and compensation by the parallel plant model. It is shown that these two approaches offer advantageous properties in the case of actuator limitations, in contrast to the commonly used PID controllers. However, when applied to integrating and unstable first-order systems, the unconstrained and possibly unobservable output disturbance signal of the second solution must be eliminated from the control loop, due to the hidden structural instability of the Smith predictor-like solutions. The modified solutions, usually referred to as filtered Smith predictor (FSP), then no longer provide a disturbance signal and thus no longer fully fit into the concept of Industry 4.0, which is focused on further optimization, predictive maintenance in dynamic systems, diagnosis, fault detection and fault identification of dynamic processes and forms the basis for the digitalization of smart production. Nevertheless, the detailed analysis of the elimination of the unstable disturbance response mode is also worth mentioning in terms of other possible solutions. The application of both approaches to the control of a thermal process shows almost equivalent quality, but with different dependencies on the tuning parameters used. It is confirmed that a more detailed identification of the controlled process and the resulting higher complexity of the control algorithms does not necessarily lead to an increase in the resulting quality of the transients, which underlines the importance of the simplified plant modelling for practice.

Two decades of functional–structural plant modelling: now addressing fundamental questions in systems biology and predictive ecology

Background Functional–structural plant models (FSPMs) explore and integrate relationships between a plant’s structure and processes that underlie its growth and development. In the last 20 years, scientists interested in functional–structural plant modelling have expanded greatly the range of topics covered and now handle dynamical models of growth and development occurring from the microscopic scale, and involving cell division in plant meristems, to the macroscopic scales of whole plants and plant communities. Scope The FSPM approach occupies a central position in plant science; it is at the crossroads of fundamental questions in systems biology and predictive ecology. This special issue of Annals of Botany features selected papers on critical areas covered by FSPMs and examples of comprehensive models that are used to solve theoretical and applied questions, ranging from developmental biology to plant phenotyping and management of plants for agronomic purposes. Altogether, they offer an opportunity to assess the progress, gaps and bottlenecks along the research path originally foreseen for FSPMs two decades ago. This review also allows discussion of current challenges of FSPMs regarding (1) integration of multidisciplinary knowledge, (2) methods for handling complex models, (3) standards to achieve interoperability and greater genericity and (4) understanding of plant functioning across scales. Conclusions This approach has demonstrated considerable progress, but has yet to reach its full potential in terms of integration and heuristic knowledge production. The research agenda of functional–structural plant modellers in the coming years should place a greater emphasis on explaining robust emergent patterns, and on the causes of possible deviation from it. Modelling such patterns could indeed fuel both generic integration across scales and transdisciplinary transfer. In particular, it could be beneficial to emergent fields of research such as model-assisted phenotyping and predictive ecology in managed ecosystems.

Advanced Process Control to Minimize Disturbance in Gas Processing Facility (GPF)

For the gas treatment process, the process that occurs is separating the gas from the components of H2S, CO2, and H2O. The separation of gases from these components uses the aid of Amine fluid and TEG fluid. The unit that important in this process are Amine and TEG Contactor. To be able to separate the three components from the gas, the mass flow rate of Amine and TEG must be controlled so that the processing can work optimally. In reality in the field, the input of this process from the well is not always steady. So, this condition becomes a disturbance from the control process. The solution to minimize the disturbance of process with Advanced Process Control (APC). Therefore, this research will design APC on Amine and TEG Contactor to improve the stability of the mass flow response of Amine and TEG. In designing APC, the plant model is required first. Plant modelling obtained with software HYSYS and validated with MATLAB. The result shows the RMSE value below 5 %. The result proved to be able to make the process more stable from before design proven by slurries settling time, steadystate errors and maximum overshoot.

CPlantBox, a whole-plant modelling framework for the simulation of water- and carbon-related processes

The interaction between carbon and flows within the vasculature is at the centre of most growth and developmental processes. Understanding how these fluxes influence each other, and how they respond to heterogeneous environmental conditions, is important to answer diverse questions in agricultural and natural ecosystem sciences. However, due to the high complexity of the plant–environment system, specific tools are needed to perform such quantitative analyses. Here, we present CPlantBox, a whole-plant modelling framework based on the root system model CRootBox. CPlantBox is capable of simulating the growth and development of a variety of plant architectures (root and shoot). In addition, the flexibility of CPlantBox enables its coupling with external modelling tools. Here, we connected the model to an existing mechanistic model of water and carbon flows in the plant, PiafMunch. The usefulness of the CPlantBox modelling framework is exemplified in five case studies. Firstly, we illustrate the range of plant structures that can be simulated using CPlantBox. In the second example, we simulated diurnal carbon and water flows, which corroborates published experimental data. In the third case study, we simulated impacts of heterogeneous environment on carbon and water flows. Finally, we showed that our modelling framework can be used to fit phloem pressure and flow speed to (published) experimental data. The CPlantBox modelling framework is open source, highly accessible and flexible. Its aim is to provide a quantitative framework for the understanding of plant–environment interaction.

Wind Power Plant Modelling in Wasp-Iv Through Ssa

This paper deals with the simulation results obtained with regard to the restricted load demand in the state of Tamil Nadu for the year 2015. There are certain approaches in long term benefits of wind power plant modelling in WASP-IV. They all have some kind of approximations such as, load modification approach and supply-side approach. In Supply-Side Approach, Wind Power Plant (WPP) is meant to have both unreliable thermal plant capacity and the hydro capacity of Run-of-River (RoR), to make this approach more effective. As Capacity Factor (CF) is estimated as 18.6% for WPP, the FOR is estimated to be 81.4%. For solar energy generation system too, such a process is applied whereby the average CF is considered to be 40% while the FOR is assumed to be 60%. In the representation of WPP as RoR hydro plant, the constraint in the form of inflow energy.