Multi-Disciplinary Design Optimization for Large-Scale Reverse Osmosis Systems

Large-scale desalination plants are complex systems with many inter-disciplinary interactions and different levels of sub-system hierarchy. Advanced complex systems design tools have been shown to have a positive impact on design in aerospace and automotive, but have generally not been used in the design of water systems. This work presents a multi-disciplinary design optimization approach to desalination system design to minimize the total water production cost of a 30,000m3/day capacity reverse osmosis plant situated in the Middle East, with a focus on comparing monolithic with distributed optimization architectures. A hierarchical multi-disciplinary model is constructed to capture the entire system’s functional components and subsystem interactions. Three different multi-disciplinary design optimization (MDO) architectures are then compared to find the optimal plant design that minimizes total water cost. The architectures include the monolithic architecture multidisciplinary feasible (MDF), individual disciplinary feasible (IDF) and the distributed architecture analytical target cascading (ATC). The results demonstrate that an MDF architecture was the most efficient for finding the optimal design, while a distributed MDO approach such as analytical target cascading is also a suitable approach for optimal design of desalination plants, but optimization performance may depend on initial conditions.

Creating Faultable Network Models of Complex Engineered Systems

This paper presents a new methodology for modeling complex engineered systems using complex networks for failure analysis. Many existing network-based modeling approaches for complex engineered systems “abstract away” the functional details to focus on the topological configuration of the system and thus do not provide adequate insight into system behavior. To model failures more adequately, we present two types of network representations of a complex engineered system: a uni-partite architectural network and a weighted bi-partite behavioral network. Whereas the architectural network describes physical inter-connectivity, the behavioral network represents the interaction between functions and variables in mathematical models of the system and its constituent components. The levels of abstraction for nodes in both network types affords the evaluation of failures involving morphology or behavior, respectively. The approach is shown with respect to a drivetrain model. Architectural and behavioral networks are compared with respect to the types of faults that can be described. We conclude with considerations that should be employed when modeling complex engineered systems as networks for the purpose of failure analysis.

Energy Optimization in Net-Zero Energy Building Clusters

Traditionally viewed as mere energy consumers, buildings have in recent years adapted, capitalizing on smart grid technologies and distributed energy resources to not only efficiently use energy, but to also output energy. This has led to the development of net-zero energy buildings, a concept which encapsulates the synergy of energy efficient buildings, smart grids, and renewable energy utilization to reach a balanced energy budget over an annual cycle. This work looks to further expand on this idea, moving beyond just individual buildings and considering net-zero at a community scale. We hypothesize that applying net-zero concepts to building communities, also known as building clusters, instead of individual buildings will result in cost effective building systems which in turn will be resilient to power disruption. To this end, this paper develops an intelligent energy optimization algorithm for demand side energy management, taking into account a multitude of factors affecting cost including comfort, energy price, Heating, Ventilation, and Air Conditioning (HVAC) system, energy storage, weather, and on-site renewable resources. A bi-level operation decision framework is presented to study the energy tradeoffs within the building cluster, with individual building energy optimization on one level and an overall net-zero energy optimization handled on the next level. The experimental results demonstrate that the proposed approach is capable of significantly shifting demand, and when viable, reducing the total energy demand within net-zero building clusters. Furthermore, the optimization framework is capable of deriving Pareto solutions for the cluster which provide valuable insight for determining suitable energy strategies.

Modular Design of Community-Scale Photovoltaic Reverse Osmosis Systems Under Uncertainty

Photovoltaic reverse osmosis (PVRO) systems can provide a viable clean water source for many remote communities. To be cost-effective, PVRO systems need to be custom-tailored for the local water demand, solar insolation, and water characteristics. Designing a custom system composed of modular components is not simple due to the large number of design choices and the variations in the sunlight and demand. This paper presents a modular design architecture, which when implemented on a low-cost PC, would enable users to configure systems from inventories of modular components. The method uses a hierarchy of filters or design rules, which can be provided in the form of an expert system, to limit the design space. The architecture then configures a system from the reduced design space using a genetic algorithm to minimize the system lifetime cost subject to system constraints. The genetic algorithm uses a detailed cost model and physics-based PVRO system model which determines the ability of the system to meet demand. Determining the ability to meet demand is challenging due to variations in water demand and solar radiation. Here, the community’s historical water demand, solar radiation history, and PVRO system physics are used in a Markov model to quantify the ability of a system to meet demand or the loss-of-water probability (LOWP). Case studies demonstrate the approach and the cost-reliability trade-off for community-scale PVRO systems. In addition, long-duration simulations are used to demonstrate the Markov model appropriately captures the uncertainty.

Component Size Optimization of a Wind-Integrated Microgrid System With Dispatch Strategy and Resource Uncertainty

The global quest for energy sustainability has motivated the development of technology for efficiently transforming various natural resources into energy. Combining these alternative energy sources with existing power systems requires systematic assessments and planning. The present study investigates the conversion of an existing power system into one with a wind-integrated microgrid. The standard approach applies wind resource assessment to determine suitable wind farm locations with high potential energy and then develops specific dispatch strategies to meet the power demand for the wind-integrated system with low cost, high reliability, and low impact on the environment. However, the uncertainty in wind resource results in fluctuating power generation. The installation of additional energy storage devices is thus needed in the dispatch strategy to ensure a stable power supply. The present work proposes a design procedure for obtaining the optimal sizing of wind turbines and storage devices considering wind resource assessment and dispatch strategy under uncertainty. Two wind models are developed from real-world wind data and apply in the proposed optimization framework. Based on comparisons of system reliability between the optimal results and real operating states, an appropriate wind model can be chosen to represent the wind characteristics of a particular region. Results show that the trend model of wind data is insufficient for wind-integrated microgrid planning because it does not consider the large variation of wind data. The wind model should include the uncertainties of wind resource in the design of a wind-integrated microgrid system to ensure high reliability of optimal results.

Enhanced Adaptive Choice-Based Conjoint Analysis Incorporating Engineering Knowledge

Conjoint analysis from marketing has been successfully integrated with engineering analysis in design for market systems. The long questionnaires needed for conjoint analysis in relatively complex design decisions can become cumbersome to the human respondents. This paper presents an adaptive questionnaire generation strategy that uses active learning and allows incorporation of engineering knowledge in order to identify efficiently designs with high probability to be optimal. The strategy is based on viewing optimal design as a group identification problem. A running example demonstrates that a good estimation of consumer preference is not always necessary for finding the optimal design and that conjoint analysis could be configured more effectively for the specific purpose of design optimization. Extending the proposed method beyond a homogeneous preference model and noiseless user responses is also discussed.

A Self-Cognizant Dynamic System Approach for Health Management: Lithium-Ion Battery Case Study

Safe and reliable operation of lithium-ion batteries as major energy storage devices is of vital importance, as unexpected battery failures could result in enormous economic and societal losses. Accurate estimation of the state-of-charge (SoC) and state-of-health (SoH) for an operating battery system, as a critical task for battery health management, greatly depends on the validity and generalizability of battery models. Due to the variability and uncertainties involved in battery design, manufacturing, and operation, developing a generally applicable battery physical model is a big challenge. To eliminate the dependency of SoC and SoH estimation on battery physical models, this paper presents a generic self-cognizant dynamic system approach for lithium-ion battery health management, which integrates an artificial neural network (ANN) with a dual extended Kalman filter (DEKF) algorithm. The ANN is trained offline to model the battery terminal voltages to be used by the DEKF. With the trained ANN, the DEKF algorithm is then employed online for SoC and SoH estimation, where voltage outputs from the trained ANN model are used in DEKF state-space equations to replace the battery physical model. Experimental results are used to demonstrate the effectiveness of the developed self-cognizant dynamic system approach for battery health management.

Optimal Configuration of Valves and Plumbing Lines in Complex Housing

In engineering design, the volume and weight of a number of systems consisting of valves and plumbing lines often need to be minimized. In current practice, this is facilitated under empirical experience with trial and error, which is time-consuming and may not yield the optimal result. This problem is intrinsically difficult due to the challenge in the formulation of optimization problem that has to be computationally tractable. In this research, we choose a sequential approach towards the design optimization, i.e., first optimizing the placement of valves under prescribed constraints to minimize the volume occupied, and then identifying the shortest paths of plumbing lines to connect the valves. In the first part, the constraints are described by analytical expressions, and two approaches of valve placement optimization are reported, i.e., a two-phase method and a simulated annealing-based method. In the second part, a three-dimensional routing algorithm is explored to connect the valves. Our case study indicates that the design can indeed be automated and design optimization can be achieved under reasonable computational cost. The outcome of this research can benefit both existing manufacturing practice and future additive manufacturing.

Integrated Decision Making in Electric Vehicle and Charging Station Location Network Design

A major barrier in consumer adoption of electric vehicles (EVs) is ‘range anxiety,’ the concern that the vehicle will run out of power at an inopportune time. Range anxiety is caused by the current relatively low electric-only operational range and sparse public charging station infrastructure. Range anxiety may be significantly mitigated if EV manufacturers and charging station operators work in partnership using a cooperative business model to balance EV performance and charging station coverage. This model is in contrast to a sequential decision making model where manufacturers bring new EVs to the market first and charging station operators decide on charging station deployment given EV specifications and market demand. This paper proposes an integrated decision making framework to assess profitability of a cooperative business models based on a multi-disciplinary optimization model that combines marketing, engineering, and operations. This model is demonstrated in a case study involving battery electric vehicle design and direct-current fast charging station location network in the State of Michigan. The expected benefits can motive both government and private enterprise actions.