Novel Heat Integration in a Methane Reformer and High Temperature PEM Fuel Cell-based mCHP System

AbstractA highly integrated and optimized heat recovery system is essential for efficient operation of a HTPEM fuel cell-based mCHP system. The main challenge with such system is to design energy efficient systems with low start-up time. Recent studies have shown that this can be achieved by proper heat integration of the balance of plant (BOP) components. This study proposed a novel scheme that optimally integrated the sub-components of a mCHP system consisting of a 2 kWel HTPEM fuel cell integrated with methane processing units, for recovery and use of the process and waste heat. A steady state system modeling and simulation of the complete mCHP system was implemented in Aspen Plus v7.2. The design of the heat recovery system was achieved with pinch analysis techniques. Heat integration results show that external cooling utility is not required and result in a 5% increase in the overall system efficienc

Multi-Attribute Tradespace Exploration as Front End for Effective Space System Design

The inability to approach systematically the high level of ambiguity present in the early design phases of space systems causes long, highly iterative, and costly design cycles. A process is introduced and described to capture decision maker preferences and use them to generate and evaluate a multitude of space system designs, while providing a common metric that can be easily communicated throughout the design enterprise. Communication channeled through formal utility interviews and analysis enables engineers to better understand the key drivers for the system and allows for a more thorough exploration of the design tradespace. Multi-attribute tradespace exploration with concurrent design, a process incorporating decision theory into model- and simulation-based design, has been applied to several space system projects at the Massachusetts Institute of Technology. Preliminary results indicate that this process can improve the quality of communication to resolve more quickly project ambiguity and to enable the engineer to discover better value designs for multiple stakeholders. The process is also integrated into a concurrent design environment to facilitate the transfer of knowledge of important drivers into higher fidelity design phases. Formal utility theory provides a mechanism to bridge the language barrier between experts of different backgrounds and differing needs, for example, scientists, engineers, managers, etc. Multi-attribute tradespace exploration with concurrent design couples decision makers more closely to the design and, most important, maintains their presence between formal reviews

Preference modelling approaches based on cumulative functions using simulation with applications

In decision making problems under uncertainty, Mean Variance Model (MVM) consistent with Expected Utility Theory (EUT) plays an important role in ranking preferences for various alternative options. Despite its wide use, this model is appropriate only when random variables representing the alternative options are normally distributed and the utility function to be maximized is quadratic; both are undesirable properties to be satisfied with actual applications. In this research, a novel methodology has been adopted in developing generalized models that can reduce the deficiency of the existing models to solve large-scale decision problems, along with applications to real-world disputes. More specifically, for eliciting preferences for pairs of alternative options, two approaches are developed: one is based on Mean Variance Model (MVM), which is consistent with Expected Utility Theory (EUT), and the second is based on Analytic Hierarchy Processes (AHP). The main innovation in the first approach is in reformulating MVM to be based on cumulative functions using simulation. Two models under this approach are introduced: the first deals with ranking preferences for pairs of lotteries/options with non-negative outcomes only while the second, which is for risk modelling, is a risk-preference model that concerns normalized lotteries representing risk factors each is obtained from a multiplication decomposition of a lottery into its mean multiplied by a risk factor. Both approximation models, which are preference-based using the determined values for expected utility, have the potential to accommodate various distribution functions with different utility functions and capable of handling decision problems especially those encountered in financial economics. The study then reformulates the second approach, AHP; a new algorithm, using simulation, introduces an approximation method that restricts the level of inherent uncertainty to a certain limit. The research further focuses on proposing an integrated preference-based AHP model introducing a novel approximation stepwise algorithm that combines the two modified approaches, namely MVM and AHP; it multiplies the determined value for expected utility, which results from implementing the modified MVM, by the one obtained from processing AHP to obtain an aggregated weight indicator. The new integrated weight scale represents an accurate and flexible tool that can be employed efficiently to solve decision making problems for possible scenarios that concern financial economics Finally, to illustrate how the integrated model can be used as a practical methodology to solve real life selection problems, this research explores the first empirical case study on Tender Selection Process (TSP) in Kurdistan Region (KR) of Iraq; it is considered as an inductive and a comprehensive investigation on TSP, which has received minimum consideration in the region, and regarded as a significant contribution to this research. The implementation of the proposed model to this case study shows that, for the evaluation of construction tenders, the integrated approach is an appropriate model, which can be easily modified to assume specific conditions of the proposed project. Using simulation, generated data allows creation of a feedback system that can be utilized for the evaluation of future projects in addition to its capability to make data handling easier and the evaluation process less complex and time consuming

A prescriptive approach to qualify and quantify customer value for value-based requirements engineering

Recently, customer-based product development is becoming a popular paradigm. Customer expectations and needs can be identified and transformed into requirements for product design with the help of various methods and tools. However, in many cases, these models fail to focus on the perceived value that is crucial when customers make the decision of purchasing a product. In this paper, a prescriptive approach to support value-based requirements engineering (RE) is proposed, describing the foundations, procedures and initial applications in the context of RE for commercial aircraft. An integrated set of techniques, such as means-ends analysis, part-whole analysis and multi-attribute utility theory is introduced in order to understand customer values in depth and width. Technically, this enables identifying the implicit value, structuring logically collected statements of customer expectations and performing value modelling and simulation. Additionally, it helps to put in place a system to measure customer satisfaction that is derived from the proposed approach. The approach offers significant potential to develop effective value creation strategies for the development of new product

Methodology for an integrated modelling of macro and microscopic processes in urban transport demand

The paper presents the theoretical formulation and the underlying assumptions for an activity-based approach of transport demand modelling. Starting with the definition of a time hierarchy of decision-making in the urban environment, rules are formulated that dictate the general hierarchic structure of individuals’ choices in the urban system. The temporal scale defines decisions for activities and their daily sequence, the geographical scale decisions associated to destination choice processes. We build activity plans (number and daily sequence of activities) from an empirical data set and calculate trip paths (time-spatial trajectories including transport modes and travel destinations) assuming consumers to maximize their utility in the decision-making process. First results of the translation of the theoretical model into a real-world application are shown for the city of Santiago, Chile

An assessment of the load modifying potential of model predictive controlled dynamic facades within the California context

California is making major strides towards meeting its greenhouse gas emission reduction goals with the transformation of its electrical grid to accommodate renewable generation, aggressive promotion of building energy efficiency, and increased emphasis on moving toward electrification of end uses (e.g., residential heating, etc.). As a result of this activity, the State is faced with significant challenges of systemwide resource adequacy, power quality and grid reliability that could be addressed in part with demand responsive (DR) load modifying strategies using controllable building technologies. Dynamic facades have the ability to potentially shift and shed loads at critical times of the day in combination with daylighting and HVAC controls. This study explores the technical potential of dynamic facades to support net load shape objectives. A model predictive controller (MPC) was designed based on reduced order thermal (Modelica) and window (Radiance) models. Using an automated workflow (involving JModelica.org and MPCPy), these models were converted and differentiated to formulate a non-linear optimization problem. A gradient-based, non-linear programming problem solver (IPOPT) was used to derive an optimal control strategy, then a post-optimization step was used to convert the solution to a discrete state for facade actuation. Continuous state modulation of the façade was also modeled. The performance of the MPC controller with and without activation of thermal mass was evaluated in a south-facing perimeter office zone with a three-zone electrochromic window for a clear sunny week during summer and winter periods in Oakland and Burbank, California. MPC strategies reduced total energy cost by 9–28% and critical coincident peak demand was reduced by up to 0.58 W/ft2-floor or 19–43% in the 4.6 m (15 ft) deep south zone on sunny summer days in Oakland compared to state-of-the-art heuristic control. Similar savings were achieved for the hotter, Burbank climate in Southern California. This outcome supports the argument that MPC control of dynamic facades can provide significant electricity cost reductions and net load management capabilities of benefit to both the building owner and evolving electrical grid