The Operating Diagram for a Two-Step Anaerobic Digestion Model

The Anaerobic Digestion Model No. 1 (ADM1) is a complex model which is widely accepted as a common platform for anaerobic process modeling and simulation. However, it has a large number of parameters and states that hinder its analytical study. Here, we consider the two-step reduced model of anaerobic digestion (AM2) which is a four-dimensional system of ordinary differential equations. The AM2 model is able to adequately capture the main dynamical behavior of the full anaerobic digestion model ADM1 and has the advantage that a complete analysis for the existence and local stability of its steady states is available. We describe its operating diagram, which is the bifurcation diagram which gives the behavior of the system with respect to the operating parameters represented by the dilution rate and the input concentrations of the substrates. This diagram, is very useful to understand the model from both the mathematical and biological points of view

Computer-aided operations engineering with integrated models of systems and operations

CONFIG 3 is a prototype software tool that supports integrated conceptual design evaluation from early in the product life cycle, by supporting isolated or integrated modeling, simulation, and analysis of the function, structure, behavior, failures and operation of system designs. Integration and reuse of models is supported in an object-oriented environment providing capabilities for graph analysis and discrete event simulation. Integration is supported among diverse modeling approaches (component view, configuration or flow path view, and procedure view) and diverse simulation and analysis approaches. Support is provided for integrated engineering in diverse design domains, including mechanical and electro-mechanical systems, distributed computer systems, and chemical processing and transport systems. CONFIG supports abstracted qualitative and symbolic modeling, for early conceptual design. System models are component structure models with operating modes, with embedded time-related behavior models. CONFIG supports failure modeling and modeling of state or configuration changes that result in dynamic changes in dependencies among components. Operations and procedure models are activity structure models that interact with system models. CONFIG is designed to support evaluation of system operability, diagnosability and fault tolerance, and analysis of the development of system effects of problems over time, including faults, failures, and procedural or environmental difficulties

Agent-Based Modeling and Simulation of Biological Systems

Agent-based modeling and simulation is a powerful technique in simulating and exploring phenomena that includes a large set of active components represented by agents. The agents are actors operating in a real system, influencing the simulated environment and influenced by the simulated environment. The agents are included in the simulation model as model components performing actions autonomously and interacting with other agents and the simulated environment to represent behaviors in the real system. In this chapter, we describe how to develop an agent-based model and simulation for biological systems in Repast Simphony platform, which is a Java-based modeling system. Repast Simphony helps developers to create a scenario tree including displays of agents, grid and continuous space, data sets, data loaders, histogram, and time charts. At the end of this chapter, we present case studies developed by our research group with references to demonstrate local behavior of biological system

Simulation Intelligence: Towards a New Generation of Scientific Methods

The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We call this merger simulation intelligence (SI), for short. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. Using this metaphor, we explore the nature of each layer of the simulation intelligence operating system stack (SI-stack) and the motifs therein: (1) Multi-physics and multi-scale modeling; (2) Surrogate modeling and emulation; (3) Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based modeling; (6) Probabilistic programming; (7) Differentiable programming; (8) Open-ended optimization; (9) Machine programming. We believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery, from solving inverse problems in synthetic biology and climate science, to directing nuclear energy experiments and predicting emergent behavior in socioeconomic settings. We elaborate on each layer of the SI-stack, detailing the state-of-art methods, presenting examples to highlight challenges and opportunities, and advocating for specific ways to advance the motifs and the synergies from their combinations. Advancing and integrating these technologies can enable a robust and efficient hypothesis-simulation-analysis type of scientific method, which we introduce with several use-cases for human-machine teaming and automated science

POWER SYSTEM ANALYSIS AT PLANT DISTRIBUTION SYSTEM

This paper presents the analysis of power system and the approaches taken to model and simulate power system of an industrial plant. The analysis is very crucial in planning, designing, and operating stages of the system to confirm all the design parameter are as per system design requirement to avoid any interruption in supply which may cause a loss in revenue as well as jeop&rdising the safety of the plant and plant personnel. To predict and understand the behavior of this system, analysis including load flow study for steady-state operation and short circuit study to calculate the maximum fault current need to be done. The main objective of this project is to develop an analysis of a practical plant model which includes all the important elements in a power system. The scope of the project includes the modeling and simulation of the industrial plant using a computer-aided simulation tool. Correct input, output data and assumption shall be made to ensure all the simulation and data interpretations are accurate. The model plant here refers to an industrial petrochemical plant. MA TLAB has been chosen to model and simulate the power system analysis due to its flexible software structure with wide selection of toolbox, model, and programme which enable user to perform engineering analysis in specific condition. In this simulation, the actual behavior of the system can be analyzed. Within a time frame of 12 months, the project is assumed feasible as it only uses established data from one of a petrochemical plant and development of the mod()! in software for simulation. Finally all the calculation result will be observe and analyze to observe the behavior of the system. The simulation also allows the engineer to assess the performance of the system during the design stage and when system is already operating

Chaotic behavior in a simple DC drive

Remarkably complex behavior, namely chaotic behavior, in a simple dc chopper-fed dc motor drive system has been investigated. An iterative map that describes the nonlinear dynamics of the system operating in the continuous conduction mode is derived. It shows that different bifurcation diagrams can be obtained by varying different system parameters, and the system exhibits not only a typical period-doubling route to chaos but also the period-3 window. Analytical modeling of period-1 and hence period-p orbits as well as their stability analysis using the characteristic multipliers are presented. Thus, those stable ranges of various system parameters can be formulated, and hence the chaotic ranges can be determined. The theoretical results are verified by using both PSpice simulation and experimental measurement.published_or_final_versio

Operator procedure verification with a rapidly reconfigurable simulator

Generating and testing procedures for controlling spacecraft subsystems composed of electro-mechanical and computationally realized elements has become a very difficult task. Before a spacecraft can be flown, mission controllers must envision a great variety of situations the flight crew may encounter during a mission and carefully construct procedures for operating the spacecraft in each possible situation. If, despite extensive pre-compilation of control procedures, an unforeseen situation arises during a mission, the mission controller must generate a new procedure for the flight crew in a limited amount of time. In such situations, the mission controller cannot systematically consider and test alternative procedures against models of the system being controlled, because the available simulator is too large and complex to reconfigure, run, and analyze quickly. A rapidly reconfigurable simulation environment that can execute a control procedure and show its effects on system behavior would greatly facilitate generation and testing of control procedures both before and during a mission. The How Things Work project at Stanford University has developed a system called DME (Device Modeling Environment) for modeling and simulating the behavior of electromechanical devices. DME was designed to facilitate model formulation and behavior simulation of device behavior including both continuous and discrete phenomena. We are currently extending DME for use in testing operator procedures, and we have built a knowledge base for modeling the Reaction Control System (RCS) of the space shuttle as a testbed. We believe that DME can facilitate design of operator procedures by providing mission controllers with a simulation environment that meets all these requirements

Modeling and simulation of crude distillation unit (CDU)

There is rapid growth in the usage and demand of crude oil in various industrial fields. Thus, the price of the petrol is rising due to the stronger-than-expected demand for petroleum products. Nowadays, simulation has become an important tool in the behavior study of almost all chemical processes. A proper modeling can bring great advantages to an industry, among them, the increase in knowledge about the process without the need to carry out the real processes. A good model is necessary to develop a proper control strategy for crude distillation unit (CDU) as it can provide more accurate behaviour study of the real system. Due to the lack of proper simulation of CDU, this research is aimed to develop modeling and simulation of CDU. Data of crude oil, the operating conditions of the involved units, and other essential data were collected and entered into the simulation software, Aspen Plus to generate the CDU model. The completed simulation of CDU was run and the results were studied. By solving model equations, the effect of different operating conditions of petroleum refining towards the yield and composition of petroleum products was determined. The higher the feed flow rate, the higher the products feed flow rates. To ensure the simulation is working, the results obtained were compared to previous works done by other researchers and were proven to be valid. Various information about the system under study were obtained easily using the CDU simulation model. The objectives of this research were accomplishe