Computational plant models or 'virtual plants' are increasingly seen as a useful tool for comprehending complex relationships between gene function, plant physiology, plant development, and the resulting plant form. The theory of L-systems, which was introduced by Lindemayer in 1968, has led to a well-established methodology for simulating the branching architecture of plants. Many current arch...

Models are used to represent aspects of the real world for specific purposes, and mathematical models have opened up new approaches in studying the behavior and complexity of biological systems. However, modeling is often time-consuming and requires significant computational resources for data development, data analysis, and simulation. Computational modeling has been successfully applied as an...

Multi-scale models can facilitate whole plant simulations by linking gene networks, protein synthesis, metabolic pathways, physiology, and growth. Whole plant models can be further integrated with ecosystem, weather, and climate models to predict how various interactions respond to environmental perturbations. These models have the potential to fill in missing mechanistic details and generate n...

The present paper discusses the development of a performance model of power generation system of a thermal plant for performance evaluation using Markov technique and probabilistic approach. The study covers two areas: development of a predictive model and evaluation of performance with the help of developed model. The present system of thermal plant under study consists of four subsystems with...

Renewable energy provides twenty percent of electricity generation worldwide. Hydroelectric power is the cheapest way to generate electricity today. It is a renewable source of energy and provides almost one-fifth of electricity in the world. Also, it generates electricity using a renewable natural resource and accounting for six percent of worldwide energy supply or about fifteen percent of th...

MIRROR PLANT uses physical plant modeling based on physical and chemical laws. However, the physical model cannot express some phenomena in plants, especially those which are not physically determined yet. We propose a hybrid model of a physical model and a statistical model based on historical plant operation data. Combining the physical model and statistical model improves the estimation accu...

backgrounds and objectives: the dispersion of particulate matter has been known as the most serious environmental pollution of cement plants. in the present work, dispersion of the particulate matter from stack of kerman cement plant was investigated using computational fluid dynamics (cfd) modeling.materials and methods: in order to study the dispersion of particulate matter from the stack, a ...

the main objective of this study is the modelling and optimization of an industrial hydrocracker unit (hu) by means of adaptive neuro fuzzy inference system (anfis) model. in this case, some data were collected from an industrial hydrocracker plant. inputs of an anfis include flow rate of fresh feed and recycle hydrogen, temperature of reactors, mole percentage of h2 and h2s, feed flow rate and...

The stability properties of discrete-time parallel and series-parallel identifiers with respect to a specific model-plant order mismatch are analyzed. While in a deterministic environment with no modeling error the two schemes give identical results, when used in a deterministic environment with modeling error their performance is different. We assume a singularly perturbed state representation...

Flux-balance modeling of plant metabolic networks provides an important complement to (13)C-based metabolic flux analysis. Flux-balance modeling is a constraints-based approach in which steady-state fluxes in a metabolic network are predicted by using optimization algorithms within an experimentally bounded solution space. In the last 2 years several flux-balance models of plant metabolism have...