Multiphase Dynamic Model Research Articles

Pandemic meltdown and economic recovery – A multi-phase dynamic model, empirics, and policy

In this paper a two-phase pandemic-economic model is proposed, with phase-specific modeling and policy variables – as suggested by the chronicle of pandemic and economic policy developments over the period 2020–2021. In a first phase, the spread of a pandemic disease is the primary concern of authorities, that still also pay attention to economic activity. A dynamic model is introduced, embedding a two-way interaction between an extended epidemic Susceptible-Infected-Recovered (SIR) model and output gap dynamics. In the second phase, posterior to lockdowns when waves fade away, monetary policy becomes the control variable, pursuing again a joint objective, of supporting a non-inflationary recovery without causing significant fatalities. We then use a standard stylized model for the macroeconomy with simplified infection dynamics, that also enter the policy objective. The two phases are thus studied in a regime change model where the control and state variables as well as the objective function are allowed to change across phases. We solve the model over a finite horizon and derive the optimal lockdown or monetary policy path that jointly minimizes pandemic and economic losses. The two-phase finite horizon decision model is empirically calibrated and numerically solved by discretization and optimization methods. In the first phase, albeit with lasting adverse effects on output, lockdown-based control can be effective in reducing infection rates, but less so when starting from a negative output gap. In the second phase, accommodative monetary policy appears to be effective on both fronts, with even an eventual need for a return to tightening as output gap closes and inflation resumes.

Realistic Simulation of Multiphase Kick Tolerance Model Promotes Safer Well Control Operations: Case Study

Nowadays, deep-water drilling is of great importance for the development of oil and gas production. It is necessary to determine the maximum pressure that the formation can withstand during well control operation. This value can be calculated using several variables, including kick tolerance. The purpose of this study is to analyze a real well control event using dynamic well control modeling software that calculates the kick tolerance based on the single-phase and multiphase dispersed kick (gas) model. The initial results show that the type of drilling fluid plays an important role in the calculation of the kick tolerance. The calculated kick tolerance using a single-phase model indicates an allowable kick volume of 56 barrels (bbls), but in reality, we had a dispersed gas kick in a synthetic oil-based mud (SOBM) drilling fluid, which requires special kick tolerance calculations based on a multiphase dynamic model. After reviewing the results of the multiphase kick tolerance simulation, it was found that the maximum gas kick volume is more than twice that of using a single-phase model in an oil-based mud drilling fluid, allowing safe well control operations and reducing the possibility of accidents.

Climate Disaster Risks—Empirics and a Multi-Phase Dynamic Model

Recent research in financial economics has shown that rare large disasters have the potential to disrupt financial sectors via the destruction of capital stocks and jumps in risk premia. These disruptions often entail negative feedback effects on the macroeconomy. Research on disaster risks has also actively been pursued in the macroeconomic models of climate change. Our paper uses insights from the former work to study disaster risks in the macroeconomics of climate change and to spell out policy needs. Empirically, the link between carbon dioxide emission and the frequency of climate related disaster is investigated using a panel data approach. The modeling part then uses a multi-phase dynamic macro model to explore the effects of rare large disasters resulting in capital losses and rising risk premia. Our proposed multi-phase dynamic model, incorporating climate-related disaster shocks and their aftermath as a distressed phase, is suitable for studying mitigation and adaptation policies as well as recovery policies.

Climate Disaster Risks – Empirics and a Multi-Phase Dynamic Model

Recent research in financial economics has shown that rare large disasters have the potential to disrupt financial sectors via the destruction of capital stocks and jumps in risk premia. These disruptions often entail negative feedback e?ects on the macroecon-omy. Research on disaster risks has also actively been pursued in the macroeconomic models of climate change. Our paper uses insights from the former work to study disaster risks in the macroeconomics of climate change and to spell out policy needs. Empirically the link between carbon dioxide emission and the frequency of climate re-lated disaster is investigated using cross-sectional and panel data. The modeling part then uses a multi-phase dynamic macro model to explore this causal nexus and the e?ects of rare large disasters resulting in capital losses and rising risk premia. Our proposed multi-phase dynamic model, incorporating climate-related disaster shocks and their aftermath as one phase, is suitable for studying mitigation and adaptation policies.

Climate Disaster Risks - Empirics and a Multi-Phase Dynamic Model

Recent research in financial economics has shown that rare large disasters have the potential to disrupt financial sectors via the destruction of capital stocks and jumps in risk premia. These disruptions often entail negative feedback effects on the macroeconomy. Research on disaster risks has also actively been pursued in the macroeconomic models of climate change. Our paper uses insights from the former work to study disaster risks in the macroeconomics of climate change and to spell out policy needs. Empirically the link between carbon dioxide emission and the frequency of climate related disaster is investigated using cross-sectional and panel data. The modeling part then uses a multi-phase dynamic macro model to explore this causal nexus and the effects of rare large disasters resulting in capital losses and rising risk premia. Our proposed multi-phase dynamic model, incorporating climate-related disaster shocks and their aftermath as one phase, is suitable for studying mitigation and adaptation policies.

Simulation of catalyst on-line replacement for Fischer-Tropsch synthesis in slurry bubble column reactor

A multi-phase dynamic model has been developed for Fischer-Tropsch synthesis in a slurry bubble column reactor operating in a heterogeneous flow regime. The catalyst particles concentration distribution, deactivation, and on-line replacement were considered in this model. The population balance equation model has been utilized to calculate the catalyst age distribution. Based on the dynamic model, the process of catalyst replacement has been investigated, and the effect of catalyst replacement on the design and operation of the reactor has been studied. A correlation between the catalyst replacement rate and the catalyst activity has been developed in this research $ \left[ {{A_C} = \left( {1 - {\alpha_\infty }} \right){e^{ - {k_d}t}} + {\alpha_\infty }} \right] $ .

Computer aided design and operation of industrial poly(vinyl chloride) batch suspension polymerisation reactors

A comprehensive multiscale, multiphase dynamic model is developed to simulate heterogeneous vinyl chloride (VCM) suspension polymerisation in industrial batch reactors. From the numerical solution of the proposed integrated model, the evolution of the molecular (i.e. molecular weight distribution, long chain branching, short chain branching, terminal double bonds) and morphological (i.e. particle size distribution, porosity) polymer properties in a PVC batch suspension polymerisation reactor can be predicted. In particular, the polymer molecular properties are determined by employing a kinetic mechanism which describes the VCM free radical polymerisation in both monomer and polymer rich phases. Semi-empirical and phenomenological expressions are used to describe the breakage and coalescence rates of dispersed monomer droplets in terms of the type and concentration of suspending agent, quality of agitation and evolution of the physical, thermodynamic and transport properties of the polymerisation system. The dynamic discretised particle population balance equation (PBE) is solved to calculate the dynamic evolution of the particle size distribution of the produced PVC. Furthermore, the primary particle size distribution (PPSD) inside the polymerising monomer droplets, which influences to a large degree the porosity of the final PVC grains, is determined by the solution of a PBE governing the nucleation, growth and aggregation of the primary particles. Finally, dynamic mass and energy balances are derived to assess the dynamic behaviour of the PVC batch suspension polymerisation reactors. The theoretical model predictions show good agreement with a comprehensive series of experimental data provided by the PVC industry. This verification of the proposed model makes it a powerful tool for the simulation of large scale PVC batch suspension polymerisation reactors.