A comparison of two data analysis techniques and their applications for modeling the carbon dioxide capture process

Improving the efficiency of the carbon dioxide (CO₂) capture process requires a good understanding of the intricate relationships among parameters involved in the process. The objective of this research is to study the nature of relationships among the key parameters using the approaches of artificial neural network and statistical analysis. Our modeling study used the three-year operational data collected from the amine-based post-combustion CO₂ capture process at the International Test Centre of CO₂ Capture (ITC) located in Regina, Saskatchewan of Canada. The goal of CO₂ capture is to capture and remove CO₂ from industrial gas streams before they are released into the atmosphere. The amine solution is used at ITC for absorbing CO₂ from the industrial flue gas, and then the CO₂ is separated from the amine solution. The amine solution recycles for further CO₂ capture and the CO₂ stream can be stored or used for other industrial purposes. This paper describes the data modeling process using the approaches of: (1) statistical analysis and (2) neural network modeling combined with sensitivity analysis. The results from the two modeling process were compared from the perspectives of predictive accuracy, inclusion of parameters, support for exploration and explication of problem space, modeling uncertainty, and involvement of experts. It was observed that the approach of neural network modeling combined with sensitivity analysis achieved much higher accuracy on predicting CO₂ production rate than the statistical study.     

Analysis of data for the carbon dioxide capture domain

To tackle the global concern for adverse impact of greenhouse gas (GHG) emissions, the post combustion carbon dioxide (CO₂) capture technology is commonly adopted for reducing industrial CO₂ emissions, for example, from power generation plants. The research on post combustion CO₂ capture has been ongoing in the last two decade, and its primary objective is to improve efficiency of the CO₂ capture process while reducing specific operating problems such as solvent degradation and corrosion. This objective requires a good understanding of the intricate relationships among parameters involved in the CO₂ capture process. From a review of the relevant literature, we observed that the most significant parameters influencing the CO₂ production rate include: heat duty, circulation rate of the solvent, CO₂ lean loading, and solvent concentration. To study the nature of relationships among the key parameters, we conducted data modeling and analysis based on the amine-based post combustion CO₂ capture process at the International Test Centre for Carbon Dioxide Capture (ITC) located in Regina, Saskatchewan of Canada. In our study, the experimental data collected from ITC from year 2003 to 2006 were analyzed using the combined approach of neural network modeling and sensitivity analysis. The neural network was trained for modeling the relationships among parameters, and the sensitivity analysis method illustrated the order of significance among the parameters. The modeling results were validated by the process experts. This paper describes the procedure of our work, and discusses the results of the analysis.     

A decision support system for computer-aided process planning

A decision support system framework is presented to aid the Decision Maker to resolve conflicting, non-commensurate objectives to set up parameters for machining operations, using a Multiple Criteria Decision Making approach.     

Modeling of the carbon dioxide capture process system using machine intelligence approaches

Improving the efficiency of the carbon dioxide (CO₂) capture process requires a good understanding of the intricate relationships among parameters involved in the process. The objective of this paper is to study the relationships among the significant parameters impacting CO₂ production. An enhanced understanding of the intricate relationships among the process parameters supports prediction and optimization, thereby improving efficiency of the CO₂ capture process. Our modeling study used the 3-year operational data collected from the amine-based post combustion CO₂ capture process system at the International Test Centre (ITC) of CO₂ Capture located in Regina, Saskatchewan of Canada. This paper describes the data modeling process using the approaches of (1) neural network modeling combined with sensitivity analysis and (2) neuro-fuzzy modeling technique. The results from the two modeling processes were compared from the perspectives of predictive accuracy, inclusion of parameters, and support for explication of problem space. We conclude from the study that the neuro-fuzzy modeling technique was able to achieve higher accuracy in predicting the CO₂ production rate than the combined approach of neural network modeling and sensitivity analysis.     

A decision support system for material and manufacturing process selection

The material and manufacturing process selection problem is a multi-attribute decision-making problem. These decisions are made during the preliminary design stages in an environment characterized by imprecise and uncertain requirements, parameters, and relationships. Material and process selection decisions must occur before design for manufacturing can begin. This paper describes a prototype material and manufacturing process selection system called MAMPS that integrates a formal multi-attribute decision model with a relational database. The decision model enables the representation of the designer's preferences over the decision factors. A compatibility rating between the product profile requirements and the alternatives stored in the database for each decision criteria is generated using possibility theory. The vector of compatibility ratings are aggregated into a single rating of that alternative's compatibility. A ranked set of compatible material and manufacturing process alternatives is output by the system. This approach has advantages over existing systems that either do not have a decision module or are not integrated with a database.     

A multi-agent system for meteorological radar data management and decision support

The continuous processing and evaluation of meteorological radar data require significant efforts by scientists, both for data processing, storage, and maintenance, and for data interpretation and visualization. To assist meteorologists and to automate a large part of these tasks, we have designed and developed Abacus, a multi-agent system for managing radar data and providing decision support. Abacus' agents undertake data management and visualization tasks, while they are also responsible for extracting statistical indicators and assessing current weather conditions. Abacus agent system identifies potentially hazardous incidents, disseminates preprocessed information over the web, and enables warning services provided via email notifications. In this paper, Abacus' agent architecture is detailed and agent communication for information diffusion is presented. Focus is also given on the customizable logical rule-bases for agent reasoning required in decision support. The platform has been tested with real-world data from the Meteorological Service of Cyprus.     

A decision support system for database evolution using data model independent architecture

The existing corpus of scientific knowledge in the field of database systems (DBS) has been concerned mostly with such problems as database technologies and system development methodologies. Relatively few efforts have been devoted to the problem of adapting an ongoing DBS in a systematic fashion. Notwithstanding the lack of sufficient prior knowledge, this adaptation problem is critical in DBS management, since a DBS should really be conceived as an evolving structure, rather than a stable one-shot phenomenon. Toward this end, this paper proposes a reliability-based decision support framework for evolving a DBS systematically. Both user satisfaction with the DBS and the usage pattern of it are monitored on a real-time basis and used for controlling it adaptively. Chance-constrained models are proposed to characterize suitable decision rules for DBS evolution decisions ranging from file reorganization to DBS restructing. A four-schema architecture is also proposed for achieving data model independence in a DBS, whereby facilitating the control and evolution of DBS.     

A PC-based decision support and expert system for labour market analysis

Due to the worsening national economy, labour market analysis will receive increasing attention in the near future. This article describes an operating Decision Support (DSS) and Expert System (ES) for labour market analysis and its designing process. The $\text{DSS}\text{ES}$ is designed following the process of Pictorial Approach to modelling, and it provides researchers with timely and credible analysis of labour market information. This $\text{DSS}\text{ES}$ uses graphic presentation to delineate employment trends and magnitude, and it takes into consideration the special needs of business research centers by its implementations via spreadsheet application to improve the learning curve. By applying rule indices directly in the spreadsheet, the ES integrates with the DSS fully, and the decision makers are able to retain access to the historical data for ad hoc computations.     

Group decision process support system for regional planning—A perspective from Japan

Regional planning has been regarded as a design activity. Usually planners focus on physical design rather than on societal issues. Nowadays, mass communication, environmental issues and social awareness lead to often complex and conflicting needs and interests of the public in regional planning. This paper focuses on the regional planning as a group problem solving process from the view of information processing. It offers an analysis of the causes of conflicts in the group decision process, and defines the characteristics of group decision process support systems     

Moving towards a collaborative decision support system for aeronautical data

The recent effort in global information standardization within the aviation industry has triggered an increased need for aviation data to be readily available, accurate and easy for stakeholders to use. Aviation management systems are generally based on old proprietary disparate systems, so there is a growing need for a system which could act as a collaborative decision support system, where the same right data can be provided at the right time to the right users. This system would not only eliminate the need to manually retrieve required information from multiple systems and reduce the possibility of human error, it would also allow the discovery of any hidden knowledge, a task otherwise not possible from separate systems. This paper presents our proposed approach to build an integrated system for data-intensive collaborative decision support. Each stage in the proposed framework is explained, including a section on the performance evaluation of the proposed system.     

A decision support system for college selection

In this changing society the importance of higher education is increasing more than ever before. The selection of an appropriate university or college is of vital importance to the student for acquisition of proper educational experience. There are thousands of universities and colleges in the United States and Canada. The information available to students about each college is plentiful but is rather tedious to be obtained. The decision process of college selection is further complicated by many factors such as tuition, location, rank, size of the universities, and so forth. These factors play an important role in the final selection of a college. Therefore, a computer-based decision support system is developed in this paper to help users make better decisions in the selection of a college.

This college selection decision support system is designed to be menu-driven and highly user-friendly with a “Help” utility. Many “what-if” scenarios are also available in this system. It can be run on any IBM XT/AT or compatible machines with a DOS environment. It will allow users to make better decisions in their college selection process.     

A manufacturing decision support system for flamecutting

The design of a Manufacturing Decision Support System (MDSS) for the control of a flamecutting operation is discussed. The MDSS incorporates the overall economics of a continuing inventory. It also takes into account the use of left-over offcuts or partial plates, and the possibility of producing as a flow shop instead of on a job shop cut-to-order basis. Some research results are available that aid the construction of such a DSS; these are mostly in the areas of two-dimensional parts' layout and torch path sequencing. The proposed DSS constructs parts' nests, generates cutting sequences, directs the use of trim margins, and updates and outputs the economics of the whole cutting process. Its major strength is its potential use at the shop floor level, using relatively inexpensive computing power to control cutting torches that are usually driven by far more expensive systems that take much longer to determine layouts and cutting sequences.     

基于神经网络的电厂烟气二氧化碳捕集过程建模

以乙醇胺(MEA)作为吸收剂的化学法捕集工艺为研究对象,利用Pro Treat软件对其进行过程模拟,在获得大量数据的基础上,运用BP神经网络建立了捕集过程的工程模型,对模型进行了有效性验证,并进行了敏感度分析,找到了影响装置性能的关键变量,对二氧化碳捕集装置的安装调试、技术改造和工艺优化可起一定的指导作用。     

A decision support system for cellular manufacturing system design

With the growth of competitive pressure in the global markets, there has been an increase in demand in industry for cellular manufacturing systems (CMSs) in order to improve productivity and process flexibility. The design of CMSs for industrial applications is a complex and knowledge intensive process as it involves the consideration of many factors including production data and process characteristics. This paper describes the development and implementation of a decision support system for the feasibility and conceptual design of CMSs. The system is based on the knowledge-based system approach, and is able to make recommendations of system feasibility, cell formation techniques and cell types. A case study is also presented to demonstrate the capability of the decision support system.     

A decision support system for the bimodal dial-a-ride problem

A bimodal dial-a-ride problem (BDARP) considered in this paper is a dial-a-ride problem that involves two transportation modes: paratransit vehicles and fixed route buses. Riders in such a system might be transferred between different transportation modes during the service process. The motivation of this research is that by efficiently coordinating paratransit vehicles with fixed route buses we can improve the accessibility and efficiency of a dial-a-ride system. In this paper, we design a decision support system (DSS) which automatically constructs efficient paratransit vehicle routes and schedules for the BDARP. This DSS has been tested using actual data from the Ann Arbor Transportation Authority (AATA) in Ann Arbor, MI. The results show that this DSS produces an average increase of 10% in the number of requests that can be accommodated and an average decrease of 10% in the number of paratransit vehicles required, as compared to the manual results where no fixed route buses are involved     

A decision support system for automotive product planning and competitive market analysis

A decision support system (DSS) for automotive product marketing, design and manufacturing in China is presented in this paper. The DSS is developed as a tool to support product planning, competitive market analysis, supply chain analysis and subsequent manufacturing systems planning and deployment. The system consists of a number of automotive related databases which provide information about manufacturers' performance in each market segment as well as production information of all existing market players in the Chinese auto industry. Product planning, one of the key modules of the DSS prototype, is highlighted in this paper. It supports decision makers in determining suitable strategies for market entry by analyzing existing competitors' status, growth estimation of each market segment, and competitive market analysis for new vehicle products. A case study for new market entry is included here to demonstrate the feasibility and effectiveness of the proposed methodology.     

A model‐driven decision support system for product risk analysis

Abstract: The paper describes a model‐driven decision support system for risk analysis in product development, based on an European Commission‐funded project. The model can be used to simulate the life cycle of a large batch of products and track products in operation and service phases with consideration of design and manufacturing phases. This provides some useful benefits – in particular, being able to generate composite results when the product life is subject to unpredictable events, which makes the system's behaviour non‐linear. A case study is conducted through a domestic appliance in the field trial from a manufacturing company, and the results indicate that the model can enable developers or manufacturers to understand the behaviour and characteristics of products or systems in the future use and to predict the cost of any changes in the pattern of their life.     

Building an organizational decision support system for Korea Telecom: A process redesign approach

Organizational decision support systems (ODSSs) are a new type of decision support systems (DSSs) focusing on organization-wide issues rather than individual, group, or departmental issues. Because of its organization-wide scope, a typical ODSS cuts across organizational functions or hierarchical layers. Thus, seamless integration with organization's diverse IS applications running on heterogeneous platforms becomes a critical issue for building a successful ODSS. In this paper, we analyzed the Korea Telecom's (KT) Operations > Maintenance (O&M) division focusing on its investment strategies. We developed a conceptual framework through process redesign, which links O&M investment decisions to performance of its operational branches across the nation. To support the above framework, we also developed a prototype for the KTOM-ODSS with an EIS-like user-friendly interface. When a complete ODSS is implemented on top of various KT transaction processing systems, it will become a critical component of the O > M Integrated Decision Support Environment (IDSE).