Alternative System Configurations Research Articles

Overview on Pressure Swing Adsorption (PSA) as CO2 Capture Technology: State-of-the-Art, Limits and Potentials

This paper provides an insight on state-of-the-art, limits and potentials of pressure swing adsorption (PSA) as CO2 capture technology into power plants. To get a complete picture both post- and pre-combustion cases are considered. The expertise developed with modeling and simulation of PSA processes in power plants stressed the need of a holistic approach to the analysis. The different domains which determine the viability of the technology are taken into account, together with their mutual influence. Accordingly, adsorbent materials characteristics, process configurations and integration strategies are investigated and discussed, in order to provide a general evaluation. In post-combustion applications, PSA has been widely assessed with regard to adsorbent materials and processes. The integration into power plant does not entail significant issues, with a good potential for retrofitting old plants. An excessively large footprint of the separation unit has been reported, which seems to question the feasibility of PSA. In pre-combustion applications, prospects for improving adsorbent materials and processes are noticed. Good potential for PSA is envisaged whit hot gas separation processes, especially with sorption-enhanced processes. Furthermore, the complexity of the arrangements in pre-combustion cases may open room for interesting alternative system configurations, like power and H2 coproduction layouts.

Energy analysis of alternative CO2 refrigeration system configurations for retail food applications in moderate and warm climates

Refrigeration systems are crucial in retail food stores to ensure appropriate merchandising of food products. This paper compares four different CO2 refrigeration system configurations in terms of cooling performance, environmental impact, power consumption and annual running costs. The systems studied were the conventional booster refrigeration system with gas bypass (reference system), the all CO2 cascade system with gas bypass, a booster system with a gas bypass compressor, and integrated cascade all CO2 system with gas bypass compressor. The weather conditions of London, UK, and Athens, Greece, were used for the modelling of energy consumption and environmental impacts to represent moderate and warm climatic conditions respectively. The control strategies for the refrigeration systems were derived from experimental tests in the laboratory on a conventional booster refrigeration system. The results from the analysis showed that the CO2 booster system with gas bypass compressor can provide best performance with 5.0% energy savings for the warm climate and 3.65% for the moderate climate, followed by the integrated cascade all CO2 system with gas bypass compressor, with 3.6% and 2.1% savings over the reference system for the warm and moderate climates respectively.

Electricity independence of the Baltic States: Present and future perspectives

Abstract Due to historical and geographical reasons, the Baltic States are strongly interconnected to power transmission grids of Russia and Belarus. Current energy security policies and electricity interconnection targets in the EU trigger the need for studying and implementing alternative electricity supply options and power system configuration schemes for the Baltic countries. In order to provide a supporting analysis for the energy policy making, with a special focus on electricity, a framework with methodologies is proposed in this paper to assess the electricity independence of the Baltic States. To comprehensively assess the electricity independence, we provided three indices: “adequacy”, “security” and “economic factor”. The proposed framework and methodologies are applied for assessing the electricity independence of the Baltic States in the present (2014) and future scenarios: mid-term (2020), and long-term (2030) time frame. The analysis results show that the planned generation capacities are adequate to cover future electricity demand in the Baltic States in 2020 and 2030. Under the current electricity grid planning, power distribution in some local areas is limited in the future scenarios. Additional grid investments are necessary to keep high security level of power supply in 2020/30.

Determination of Heat Transfer Characteristics of Solar Thermal Collectors as Heat Source for a Residential Heat Pump

With reducing energy demand and required installed mechanical system power of modern residences, alternate heat pump system configurations with a possible increased economic viability emerge. Against this background, this paper presents a numerically examined energy feasibility study of a solar driven heat pump system for a low energy residence in a moderate climate, where a covered flat plate solar collector served as the sole low temperature heat source. A parametric study on the ambient-to-solarfluid heat transfer coefficient was conducted to determine the required solar collector heat transfer characteristics in this system setup. Moreover, solar collector area and storage tank volume were varied to investigate their impact on the system performance. A new performance indicator “availability” was defined to assess the contribution of the solar collector as low temperature energy source of the heat pump. Results showed that the use of a solar collector as low temperature heat source was feasible if its heat transfer rate (UA-value) was 200 W/K or higher. Achieving this value with a realistic solar collector area (A-value) required an increase of the overall ambient-to-solarfluid heat transfer coefficient (U-value) with a factor 6–8 compared to the base case with heat exchange between covered solar collector and ambient.

Development and application of integrative modeling tools in support of food-energy-water nexus planning—a research agenda

This paper outlines a research agenda on the development of analytical tools to support the analysis of integrated food, energy, and water (FEW) systems. The thrust of this agenda is on increasing awareness and building capacity on interdisciplinary data and mathematical modeling toward integrated planning and identification/evaluation of trade-offs and synergies in developing such systems. The research agenda consists of development of principles, algorithms, and model formulations for understanding and evaluating the potential of implementing FEW nexus approaches within a systems perspective. The proposed agenda also stresses the need for integrating areas of disciplinary expertise, the ability to identify and address shared needs of FEW stakeholders, and facilitating tailored analyses over different geographical regions and temporal scales. Outputs and products of this research are quantitative tools that focus on upstream sector planning in order to identify primary opportunities and constraints to food, energy, and water system development, indicating priorities for more detailed analysis as well as providing characterization of alternative system configuration that meet integrated FEW objectives. This research agenda should also result in an improved understanding of economic and social trade-offs among competing FEW priorities; responses to the research questions contained in this agenda are bound to support decision-making in integrated FEW system planning and particularly prioritization of FEW investments.

Modelling to generate alternatives with an energy system optimization model

Energy system optimization models (ESOMs) should be used in an interactive way to uncover knife-edge solutions, explore alternative system configurations, and suggest different ways to achieve policy objectives under conditions of deep uncertainty. In this paper, we do so by employing an existing optimization technique called modeling to generate alternatives (MGA), which involves a change in the model structure in order to systematically explore the near-optimal decision space. The MGA capability is incorporated into Tools for Energy Model Optimization and Analysis (Temoa), an open source framework that also includes a technology rich, bottom up ESOM. In this analysis, Temoa is used to explore alternative energy futures in a simplified single region energy system that represents the U.S. electric sector and a portion of the light duty transport sector. Given the dataset limitations, we place greater emphasis on the methodological approach rather than specific results.

Multi-criteria selection and thermo-economic optimization of Organic Rankine Cycle system for a marine application

The purpose of this work is to investigate the possibility of exploiting the low-temperature thermal energy released from marine propulsion engines by an Organic Rankine Cycle system (ORCs) and determine an optimal design of such a system for a particular engine as an example. At first, taking into consideration thermodynamic properties of several fluids and regulations applicable in the marine environment, proper organic Rankine fluids are selected for further consideration. Alternative system configurations with different fluids are assessed with a multi-criteria approach based on performance indexes. The system with the highest value of the Composite Performance Indicator is further studied. The optimal synthesis, design and operation is determined with maximization of the Net Present Value as the objective function. The optimization problem is solved with a combination of a Genetic Algorithm and a Sequential Quadratic Programming Algorithm. Furthermore, a sensitivity analysis is performed with respect to important parameters. It is interesting to note how the optimal synthesis (configuration) of the system changes automatically with changing operation profile of the propulsion engine. The presentation closes with suggestions for further work.

Rationalism with a dose of empiricism: combining goal reasoning and case-based reasoning for self-adaptive software systems

Requirements-driven approaches provide an effective mechanism for self-adaptive systems by reasoning over their runtime requirements models to make adaptation decisions. However, such approaches usually assume that the relations among alternative system configurations, environmental parameters and requirements are clearly understood, which is often not true. Moreover, they do not consider the influence of the current configuration of an executing system on adaptation decisions. In this paper, we propose an improved requirements-driven self-adaptation approach that combines goal reasoning and case-based reasoning. In the approach, past experiences of successful adaptations are retained as adaptation cases, which are described by not only requirements violations and contexts, but also currently deployed system configurations. The approach does not depend on a set of original adaptation cases, but employs goal reasoning to provide adaptation solutions when no similar cases are available. Case-based reasoning is used to provide more precise adaptation decisions that better reflect the complex relations among requirements violations, contexts, and current system configurations by utilizing past experiences. To prevent case-based reasoning from getting trapped in suboptimal adaptation solutions, an additional case mutation mechanism is introduced to mutate existing adaptation solutions when necessary. We conduct an experimental study with an online shopping benchmark to evaluate the effectiveness of our approach. The results show that our approach outperforms both a requirements-driven approach and a case-based approach in terms of satisfaction level of quality constraints. The results also confirm the effectiveness of case mutation for producing better adaptation solutions. In addition, we empirically investigate the evolution process of adaptation solutions. The evolution analysis reveals some general evolution trends of adaptation solutions such as different evolution phases.

The added value of system robustness analysis for flood risk management illustrated by a case on the IJssel River

Abstract. Decision makers in fluvial flood risk management increasingly acknowledge that they have to prepare for extreme events. Flood risk is the most common basis on which to compare flood risk-reducing strategies. To take uncertainties into account the criteria of robustness and flexibility are advocated as well. This paper discusses the added value of robustness as an additional decision criterion compared to single-value flood risk only. We do so by quantifying flood risk and system robustness for alternative system configurations of the IJssel River valley in the Netherlands. We found that robustness analysis has added value in three respects: (1) it does not require assumptions on current and future flood probabilities, since flood consequences are shown as a function of discharge; (2) it shows the sensitivity of the system to varying discharges; and (3) it supports a discussion on the acceptability of flood damage. We conclude that robustness analysis is a valuable addition to flood risk analysis in support of long-term decision-making on flood risk management.

Using patient flow simulation to improve access at a multidisciplinary sleep centre.

The lack of timely access to diagnosis and treatment for sleep disorders is well described, but little attention has been paid to understanding how multiple system constraints contribute to long waiting times. The objectives of this study were to identify system constraints leading to long waiting times at a multidisciplinary sleep centre, and to use patient flow simulation modelling to test solutions that could improve access. Discrete-event simulation models of patient flow were constructed using historical data from 150 patients referred to the sleep centre, and used to both examine reasons for access delays and to test alternative system configurations that were predicted by administrators to reduce waiting times. Four possible solutions were modelled and compared with baseline, including addition of capacity to different areas at the sleep centre and elimination of prioritization by urgency. Within the model, adding physician capacity improved time from patient referral to initial physician appointment, but worsened time from polysomnography requisition to test completion, and had no effect on time from patient referral to treatment initiation. Adding respiratory therapist did not improve model performance compared with baseline. Eliminating triage prioritization worsened time to physician assessment and treatment initiation for urgent patients without improving waiting times overall. This study demonstrates that discrete-event simulation can identify multiple constraints in access-limited healthcare systems and allow suggested solutions to be tested before implementation. The model of this sleep centre predicted that investments in capacity expansion proposed by administrators would not reduce the time to a clinically meaningful patient outcome.

Resilience-based failure mode effects and criticality analysis for regional water supply system

An alternative risk assessment method, known as failure mode effects and criticality analysis (FMECA), is demonstrated on the regional water supply systems (RWSS) in Tucson, AZ, USA that combines delivery of potable and reclaimed water and conveyance of wastewater to a developing area within the Tucson RWSS. The goal of FMECA is to examine the volumetric severity of a component failure on the overall system function by modeling the system under alternative failure modes. Within FMECA, the Risk Priority Number (RPN) is applied to compare the risk criticality between components' failures. To complete FMECA, the Tucson RWSS is represented in a network flow model that optimally allocates flows between sources and demand points to minimize operational costs. Potential failure mode consequences are evaluated from the flow model as the volume of water not delivered to users if the component is unavailable. The volumetric severity of the failure event is converted to an ordinal value using stakeholder judgment. Likelihood of each failure mode is similarly defined by stakeholders on a 1–10 scale. The RPN is then computed as the product of the severity and likelihood. RPN values for all failure modes are then ranked to assess the most critical elements. Alternative system configurations are examined to assess the value of redundancies on the Tucson RWSS resilience.

Optimal EEG feature selection from average distance between events and non-events.

Biosignal classification systems often have to deal with extraneous features, highly imbalanced datasets, and a low SNR. A robust feature selection/reduction method is a crucial step in this process. Sets of artificial data were generated to test a prototype EEG-based microsleep detection system, consisting of a combination of EEG and 2-s bursts of 15-Hz sinusoids of varied signal-to-noise ratios (SNRs) ranging from 16 to 0.03. The balance between events and non-events was varied between evenly balanced and highly imbalanced (e.g., events occurring only 2% of the time). Features were spectral estimates of various EEG bands (e.g., alpha band power) or ratios between them. A total of 34 features for each of the 16 channels yielded a total of 544 features. Five minutes of EEG from a total of eight subjects were used in the generation of the artificial data. Several feature reduction and classifier structures were investigated. Taking only a single feature corresponding to the maximum of average distance between events and non-events (ADEN) on unbalanced data yielded a phi correlation of 0.94 on the mock data with an SNR of 0.3, compared with a phi coefficient of 0.00 for principal component analysis (PCA). ADEN consistently outperformed alternative system configurations, independent of the classifier utilized. While ADEN's high performance may be due to the nature of the artificial dataset, this simulation has demonstrated strong potential compared to other feature selection/reduction methods.

Characterising heavy-duty fuel systems: a critique of industry practice and challenges

Fuel system components must perform optimally to ensure the maximum fuel economy and the minimum emissions. The key to optimised performance is effective characterisation of the system in the factory. This paper describes methods for discerning the features of a fuel injection system’s performance during built-unit testing. First, a background is given, highlighting the impact of environmental legislation. Next, specifications for a modern fuel system and key functionality characteristics are identified, and a brief overview of the alternative system configurations offered. The injector function is then discussed, with the importance of the key subcomponents being explained. Methods of testing and characterising the fuel injector performance in use are then described. Finally, there is discussion on the future of fuel system testing, and how developing challenges such as measuring the injection timing and the fuelling quantity in high-pressure, high-temperature and high-vibration environments might be overcome with better instruments and novel testing strategies including a paradigm shift away from steady-state testing to fully dynamic system testing.

Multi-objective optimization of waste and resource management in industrial networks – Part I: Model description

This article presents a general multi-objective mixed-integer linear programming (MILP) optimization model aimed at providing decision support for waste and resources management in industrial networks. The MILP model combines material flow analysis, process models of waste treatments and other industrial processes, life cycle assessment, and mathematical optimization techniques within a unified framework. The optimization is based on a simplified representation of industrial networks that makes use of linear process models to describe the flows of mass and energy. Waste-specific characteristics, e.g. heating value or heavy metal contamination, are considered explicitly along with potential technologies or process configurations. The systems perspective, including both provision of waste treatment and industrial production, enables constraints imposed upon the systems, e.g. available treatment capacities, to be explicitly considered in the model. The model output is a set of alternative system configurations in terms of distribution of waste and resources that optimize environmental and economic performance. The MILP also enables quantification of the improvement potential compared to a given reference state. Trade-offs between conflicting objectives are identified through the generation of a set of Pareto-efficient solutions. This information supports the decision making process by revealing the quantified performance of the efficient trade-offs without relying on weighting being expressed prior to the analysis. Key features of the modeling approach are illustrated in a hypothetical case. The optimization model described in this article is applied in a subsequent paper (Part II) to assess and optimize the thermal treatment of sewage sludge in a region in Switzerland.

Modeling the integrated roles of insurance and retrofit in managing natural disaster risk: a multi-stakeholder perspective

This paper introduces a new modeling framework to understand and improve regional natural disaster risk management in the USA, including the interactions among key stakeholders and between the two important risk management mechanisms of insurance and retrofit. The framework includes a stochastic programming optimization to represent insurer decisions, which interacts with a utility-based model of individual homeowners' decisions to insure and/or retrofit. Reinsurer and government roles are represented as inputs, and the decision models are integrated with a detailed regional catastrophe loss estimation model. This modeling framework is applied to a full-scale, realistic case study for hurricane risk to residential buildings in Eastern North Carolina. Several alternative system configurations are considered that affect the incentives for adoption of alternative risk management methods. They include providing a government subsidy for insured homeowners to encourage retrofit, providing both a government subsidy and insurance rebate to reduce retrofit costs, and mandating insurance purchase with a cap on insurance premiums. For each configuration, outcomes are presented from the perspectives of all key stakeholders--primary insurer, homeowners (insured and uninsured, in high- and low-risk areas), reinsurers, and the government. RESULTS suggest that it is possible to design policies in which all stakeholders can be better off simultaneously. Retrofit incentives for insured homeowners can be effective in linking and strengthening the benefits of retrofit and insurance. Mandatory insurance coupled with capped profit loading factors and possibly retrofit rebates from the insurer to the homeowner can also reduce overall system risk. Language: en

A Causal Perspective on the Analysis of Signal and Noise Correlations and Their Role in Population Coding

The role of correlations between neuronal responses is crucial to understanding the neural code. A framework used to study this role comprises a breakdown of the mutual information between stimuli and responses into terms that aim to account for different coding modalities and the distinction between different notions of independence. Here we complete the list of types of independence and distinguish activity independence (related to total correlations), conditional independence (related to noise correlations), signal independence (related to signal correlations), coding independence (related to information transmission), and information independence (related to redundancy). For each type, we identify the probabilistic criterion that defines it, indicate the information-theoretic measure used as statistic to test for it, and provide a graphical criterion to recognize the causal configurations of stimuli and responses that lead to its existence. Using this causal analysis, we first provide sufficiency conditions relating these types. Second, we differentiate the use of the measures as statistics to test for the existence of independence from their use for quantification. We indicate that signal and noise correlation cannot be quantified separately. Third, we explicitly define alternative system configurations used to construct the measures, in which noise correlations or noise and signal correlations are eliminated. Accordingly, we examine which measures are meaningful only as a comparison across configurations and which ones provide a characterization of the actually observed responses without resorting to other configurations. Fourth, we compare the commonly used nonparametric approach to eliminate noise correlations with a functional (model-based) approach, showing that the former approach does not remove those effects of noise correlations captured by the tuning properties of the individual neurons, and implies nonlocal causal structure manipulations. These results improve the interpretation of the measures on the framework and help in understanding how to apply it to analyze the role of correlations.

An empirical typology of private child and family serving agencies

Differences in how services are organized and delivered can contribute significantly to variation in outcomes experienced by children and families. However, few comparative studies identify the strengths and limitations of alternative delivery system configurations. The current study provides the first empirical typology of private agencies involved with the formal child welfare system. Data collected in 2011 from a national sample of private agencies were used to classify agencies into five distinct groups based on internal management capacity, service diversification, integration, and policy advocacy. Findings reveal considerable heterogeneity in the population of private child and family serving agencies. Cross-group comparisons suggest that differences in agencies' strategic and structural characteristics correlated with agency directors' perceptions of different pressures in their external environment. Future research can use this typology to better understand local service systems and the extent to which different agency strategies affect performance and other outcomes. Such information has implications for public agency contracting decisions and could inform system-level assessment and planning of services for children and families.

Combined simulation of a deep ground source heat exchanger and an office building

It is analyzed, if and to which share the deep borehole heat exchanger (BHE) at RWTH Aachen University in its current state is capable of heating an office building located at its top, called SuperC. Furthermore, possible modifications to the BHE and to the hydraulic connection to the building's heating system are discussed.Models of the BHE and of the building are each validated with measurement data and than connected to each other to carry out system simulations for one heating period. It is found that the BHE in its current state can support the heating system by a notable share of the annual heating demand by supplying the low temperature heating systems of the building. An alternative system configuration and a different inner BHE pipe provide the opportunity of higher supply temperatures leading to a higher fraction of geothermal heating.

Simulation study on biodiesel production by reactive distillation with methanol at high pressure and temperature: Impact on costs and pollutant emissions

Recently, a two-step biodiesel production process which uses short-chain alcohols at supercritical conditions has been proposed. In addition, literature reports suggest that the COSMO-SAC thermodynamic model is a suitable alternative for the prediction of VLE for supercritical methanol/methyl esters mixtures. Thus, in this work a simulation study of the two-step supercritical method for the production of biodiesel is performed by using the COSMO-SAC model. Further, alternative system configurations for biodiesel production based on reactive distillation are proposed and their total emissions are compared to those corresponding to the conventional catalytic method. The study demonstrates the benefits of using reactive distillation for the esterification step and discusses the environmental impact of the supercritical production process. It has been found that the intensified alternatives reduce the emissions considerably and, through the reuse of the excess methanol, the emissions level of the supercritical process can be compared to those of the catalytic method.

Architecture exploration and timing estimation during early design phases

Selecting the right computation hardware and configuration at the beginning of an industrial project is an important and highly risky task, which is usually done without much tool support, based on experience gathered from previous projects. We present TimingExplorer—a tool to assist in the exploration of alternative system configurations in early design phases. It is based on AbsInt’s WCET Analyser aiT and provides a selection of parametrisable cores that represent typical architectures of interest. Once some (representative) source code is available, TimingExplorer enables its users to take an informed decision which processor configurations are best suited for their needs. The usage of TimingExplorer facilitates the process of determining what processors to use and reduces the risk of timing problems becoming obvious only late in the development cycle and requiring a redesign of large parts of the system.