A modular hybrid simulation framework for complex manufacturing system design

For complex manufacturing systems, the current hybrid Agent-Based Modelling and Discrete Event Simulation (ABM–DES) frameworks are limited to component and system levels of representation and present a degree of static complexity to study optimal resource planning. To address these limitations, a modular hybrid simulation framework for complex manufacturing system design is presented. A manufacturing system with highly regulated and manual handling processes, composed of multiple repeating modules, is considered. In this framework, the concept of modular hybrid ABM–DES technique is introduced to demonstrate a novel simulation method using a dynamic system of parallel multi-agent discrete events. In this context, to create a modular model, the stochastic finite dynamical system is extended to allow the description of discrete event states inside the agent for manufacturing repeating modules (meso level). Moreover, dynamic complexity regarding uncertain processing time and resources is considered. This framework guides the user step-by-step through the system design and modular hybrid model. A real case study in the cell and gene therapy industry is conducted to test the validity of the framework. The simulation results are compared against the data from the studied case; excellent agreement with 1.038% error margin is found in terms of the company performance. The optimal resource planning and the uncertainty of the processing time for manufacturing phases (exo level), in the presence of dynamic complexity is calculated

A community-scale hybrid energy system integrating biomass for localised solid waste and renewable energy solution: Evaluations in UK and Bulgaria

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Growing pace of urban living is expected to simultaneously aggravate both the waste and the energy crises. This study presents feasibility assessment of a community scale hybrid renewable energy system (HRES) utilising biomass to serve the local energy needs while reducing the household solid waste volume. A modelling framework is presented and evaluated for a biomass HRES, comprising of a Wind turbine-PV Array-Biogas generator-Battery system, applied to two European cities - Gateshead (UK) and Sofia (Bulgaria) - accounting for their distinct domestic biowaste profiles, renewable resources and energy practices. Biogas generator is found to make the most substantial share of electricity generation (up to 60–65% of total), hence offering a stable community-scale basal electricity generation potential, alongside reduction in disposal costs of local solid waste. Net present cost for the biomass-integrated HRESs is found within 5% of each other, despite significant differences in the availability of solar and wind resources at the two sites. Based on a survey questionnaire targeting construction companies and energy solution developers, project costs and planning regulatory red tapes were identified as the two common implementation challenges in both the countries, with lack of awareness of HRES as a further limitation in Bulgaria, impeding wider uptake of this initiative

Econometric Methods for Endogenously Sampled Time Series: The Case of Commodity Price Speculation in the Steel Market

This paper studies the econometric problems associated with estimation of a stochastic process that is endogenously sampled. Our interest is to infer the law of motion of a discrete-time stochastic process {p_t} that is observed only at a subset of times {t_1,...,t_n} that depend on the outcome of a probabilistic sampling rule that depends on the history of the process as well as other observed covariates x_t. We focus on a particular example where p_t denotes the daily wholesale price of a standardized steel product. However there are no formal exchanges or centralized markets where steel is traded and pt can be observed. Instead nearly all steel transaction prices are a result of private bilateral negotiations between buyers and sellers, typically intermediated by middlemen known as steel service centers. Even though there is no central record of daily transactions prices in the steel market, we do observe transaction prices for a particular firm -- a steel service center that purchases large quantities of steel in the wholesale market for subsequent resale in the retail market. The endogenous sampling problem arises from the fact that the firm only records p_t on the days that it purchases steel. We present a parametric analysis of this problem under the assumption that the timing of steel purchases is part of an optimal trading strategy that maximizes the firm's expected discounted trading profits. We derive a parametric partial information maximum likelihood (PIML) estimator that solves the endogenous sampling problem and efficiently estimates the unknown parameters of a Markov transition probability that determines the law of motion for the underlying {p_t} process. The PIML estimator also yields estimates of the structural parameters that determine the optimal trading rule. We also introduce an alternative consistent, less efficient, but computationally simpler simulated minimum distance (SMD) estimator that avoids high dimensional numerical integrations required by the PIML estimator. Using the SMD estimator, we provide estimates of a truncated lognormal AR(1) model of the wholesale price processes for particular types of steel plate. We use this to infer the share of the middleman's discounted profits that are due to markups paid by its retail customers, and the share due to price speculation. The latter measures the firm's success in forecasting steel prices and in timing its purchases in order to "buy low and sell high'." The more successful the firm is in speculation (i.e., in strategically timing its purchases), the more serious are the potential biases that would result from failing to account for the endogeneity of the sampling process.Endogenous sampling, Markov processes, Maximum likelihood, Simulation estimation

Econometric Methods for Endogenously Sampled Time Series: The Case of Commodity Price Speculation in the Steel Market

This paper studies the econometric problems associated with estimation of a stochastic process that is endogenously sampled. Our interest is to infer the law of motion of a discrete-time stochastic process {pt} that is observed only at a subset of times {t1,..., tn} that depend on the outcome of a probabilistic sampling rule that depends on the history of the process as well as other observed covariates xt . We focus on a particular example where pt denotes the daily wholesale price of a standardized steel product. However there are no formal exchanges or centralized markets where steel is traded and pt can be observed. Instead nearly all steel transaction prices are a result of private bilateral negotiations between buyers and sellers, typically intermediated by middlemen known as steel service centers. Even though there is no central record of daily transactions prices in the steel market, we do observe transaction prices for a particular firm -- a steel service center that purchases large quantities of steel in the wholesale market for subsequent resale in the retail market. The endogenous sampling problem arises from the fact that the firm only records pt on the days that it purchases steel. We present a parametric analysis of this problem under the assumption that the timing of steel purchases is part of an optimal trading strategy that maximizes the firm's expected discounted trading profits. We derive a parametric partial information maximum likelihood (PIML) estimator that solves the endogenous sampling problem and efficiently estimates the unknown parameters of a Markov transition probability that determines the law of motion for the underlying {pt} process. The PIML estimator also yields estimates of the structural parameters that determine the optimal trading rule. We also introduce an alternative consistent, less efficient, but computationally simpler simulated minimum distance (SMD) estimator that avoids high dimensional numerical integrations required by the PIML estimator. Using the SMD estimator, we provide estimates of a truncated lognormal AR(1) model of the wholesale price processes for particular types of steel plate. We use this to infer the share of the middleman's discounted profits that are due to markups paid by its retail customers, and the share due to price speculation. The latter measures the firm's success in forecasting steel prices and in timing its purchases in order to buy low and sell high'. The more successful the firm is in speculation (i.e. in strategically timing its purchases), the more serious are the potential biases that would result from failing to account for the endogeneity of the sampling process.

Using Project Management methodologies in a CubeSat project

The CubeSat designation was developed in 1999 at Stanford University and the California Polytech State University to facilitate the participation of students in academic satellite projects. Nowadays CubeSats are a common reality, facing an increasing of projects and well succeed deployments, which made some authors consider the possibility of replace and renew the existing constellations of bigger satellites for this type of small, low-cost satellites, maintaining or increasing their actual capabilities for a similar price. The University of Beira Interior and its students pretends to create an entire CubeSat, which would be capable to test the Radio Frequency Blackout during re-entry on Earth atmosphere had arisen, to support a numerical model already in development and testing at University of Beira Interior. The purpose of this dissertation is to implement Project Management methodologies, focusing on planning, schedule, documentation and data, and risk management, on a CubeSat project developed in cooperation with University and industry. Project management was implemented after the project kick-off, being necessary to organise the work already done. The first step was the elaboration of a schedule for the tasks (already done and further work), a preliminary risk management, and the essential documentation management to allow the proper monitoring of the project from the team and external entities.Em 1999, na Universidade de Stanford em cooperação com a California Polytech State University, desenvolveu o conceito de CubeSat para facilitar a participação de estudantes em projetos académicos espaciais. Atualmente os CubeSats são cada vez mais comuns, com um aumento do número de projetos e lançamentos bem-sucedidos cada vez maior, havendo já quem defenda a possibilidade de utilizar este tipo de satélites pequenos, “low-cost” para a substituição e renovação de constelações de satélites maiores, mantendo ou aumentando as capacidades atuais, com um custo similar. A Universidade da Beira Interior e os seus alunos pretendem desenvolver um CubeSat capaz de testar os efeitos do plasma no fenómeno de “blackout” durante a reentrada na atmosfera terrestre de um objeto, numa tentativa de validar um modelo numérico que já se encontra em estudo e desenvolvimento na Universidade. O propósito desta dissertação é a implementação de metodologias de gestão de projeto, focada na gestão do planeamento, da cronologia, da documentação e do risco, de um projeto de CubeSat desenvolvido em cooperação com a indústria. A introdução de uma metodologia foi implementada com o projeto já em desenvolvimento, o que dificultou a tarefa de gestão do projeto, sendo necessário começar por gerir todo o trabalho já realizado. Posto isto, a tarefa inicial foi a elaboração de um cronograma visando as tarefas já realizadas e a realizar no futuro, seguindo-se um levantamento preliminar dos riscos inerentes ao projeto, bem como a gestão documental necessária para que se torne possível um acompanhamento do projeto a membros exteriores à equipa de trabalho

An overview of current status of carbon dioxide capture and storage technologies

AbstractGlobal warming and climate change concerns have triggered global efforts to reduce the concentration of atmospheric carbon dioxide (CO2). Carbon dioxide capture and storage (CCS) is considered a crucial strategy for meeting CO2 emission reduction targets. In this paper, various aspects of CCS are reviewed and discussed including the state of the art technologies for CO2 capture, separation, transport, storage, leakage, monitoring, and life cycle analysis. The selection of specific CO2 capture technology heavily depends on the type of CO2 generating plant and fuel used. Among those CO2 separation processes, absorption is the most mature and commonly adopted due to its higher efficiency and lower cost. Pipeline is considered to be the most viable solution for large volume of CO2 transport. Among those geological formations for CO2 storage, enhanced oil recovery is mature and has been practiced for many years but its economical viability for anthropogenic sources needs to be demonstrated. There are growing interests in CO2 storage in saline aquifers due to their enormous potential storage capacity and several projects are in the pipeline for demonstration of its viability. There are multiple hurdles to CCS deployment including the absence of a clear business case for CCS investment and the absence of robust economic incentives to support the additional high capital and operating costs of the whole CCS process