Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Finding alternative right of way using multi-criteria decision analysis based on least cost path: A case study of 20’’Anoh pipeline

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesA multi-criteria decision analysis was conducted using a geographic information system (GIS) coupled with analytical hierarchy process (AHP) methods to evaluate and prioritize the pipeline project areas for Assa North Ohaji South. Alternative Right-of-Ways (ROWs) with two optimal routes were determined and compared with the existing ROWs. During the analysis, several criteria were considered to determine the least cost alternative ROW, including slope, geology, waterbodies, roads, land use, and land cover. The optimum route for connecting the source and destination was then determined. The LANDSAT 8 imageries of the study area were processed and classified into various land use and land cover types, which were then modeled using ArcMap 10.8 GIS software for routing analysis. It was used in the study to demonstrate the efficiency of MCDA LCP and AHP integration in generating optimum routes for the ANOH project. By avoiding steep slopes, built-up areas, and waterbodies, the optimal route avoided the limitations of the existing ROW. This route has a 22% reduction in length and will decrease construction costs, which is an indication of its efficiency

Applications of AHP, FAHP, BWM, Entropy, and CRITIC Methods in Electrohydraulic Forming Process Parametric Evaluation for Automotive Panels Using the 1100 Aluminum Alloy Sheets

Although multicriteria selection methods are flexible and extensively used in machining, less attention has been paid to their comprehensive test performance in the electrohydraulic forming process. In this study, five new applications of multicriteria selection methods are proposed to analyze available parameters in the electrohydraulic forming process and select parameters best suited for further analysis and improvement of the process. The analyzed parameters are the stand-off distance, electrode gap, voltage, and medium, while the multicriteria methods are the AHP, FAHP, BMW, entropy, and CRITIC. The proposed methods were demonstrated on experimental data from the literature utilizing an impulse magnetizer system (walker type). For each method, the prioritized parametric results were obtained. All the methods assign the first position to the medium as a parameter with consensus on the voltage parameter has the worst (lowest) value of weights in all the methods. The weights of the medium parameter for the best results are 0.5030 (AHP method), 0.5600 (FAHP method), 0.5230 (best-worst method), 0.4090 (entropy method), and 0.5000 (CRITIC method). The worst parameter for all the methods is the voltage of 0.0320 (FAHP method). The results obtained from the proposed applications were compared with one another and found to be effective for multicriteria selection decisions. This article offers new methods to establish the parametric values of the electrohydraulic forming process for machining composites made of AA1100 sheets

Integrating bioprocesses into industrial complexes for sustainable development

The objective of this research is to propose, develop and demonstrate a methodology for the optimal integration of bioprocesses in an existing chemical production complex. Chemical complex optimization is determining the optimal configuration of chemical plants in a superstructure of possible plants based on economic, environmental and sustainable criteria objective function (triple bottomline) and solves a mixed integer non linear programming problem. This research demonstrated the transition of production of chemicals from non-renewable to renewable feedstock. A conceptual design of biochemical processes was converted to five industrial scale designs in Aspen HYSYS® process simulator. Fourteen input-output block models were created from the designs based on the mass and energy relations. A superstructure of plants was formed by integrating the bioprocess models into a base case of existing plants in the lower Mississippi River corridor. Carbon dioxide produced from the integrated complex was used for algae oil and new chemicals production. The superstructure had 978 equality constraints, 91 inequality constraints, 969 continuous variables and 25 binary variables. The optimal solution gave a triple bottomline profit of $1,650 million per year from the base case solution of$854 million per year (93% increase). Raw material costs in the optimal solution decreased by 31% due to the exclusion of the costly ethylbenzene process. The utility costs for the complex increased to $46 million per year from$12 million per year. The sustainable costs to the society decreased to $10 million per year from$18 million per year (44% decrease). The bioprocesses increased the pure carbon dioxide sources to 1.07 million metric tons per year from 0.75 million metric tons per year for the base case (43% increase). The pure carbon dioxide vented to the atmosphere was reduced to zero in the optimal structure from 0.61 million metric tons per year (100% decrease) by consumption in the complex. The methodology can be used by decision makers to evaluate energy efficient and environmentally acceptable plants and have new products from greenhouse gases. Based on these results, the methodology could be applied to other chemical complexes in the world for reduced emissions and energy savings

Life cycle assessment (LCA) applied to the process industry: a review

Purpose : Life cycle assessment (LCA) methodology is a well-established analytical method to quantify environmental impacts, which has been mainly applied to products. However, recent literature would suggest that it has also the potential as an analysis and design tool for processes, and stresses that one of the biggest challenges of this decade in the field of process systems engineering (PSE) is the development of tools for environmental considerations. Method : This article attempts to give an overview of the integration of LCA methodology in the context of industrial ecology, and focuses on the use of this methodology for environmental considerations concerning process design and optimization. Results : The review identifies that LCA is often used as a multi-objective optimization of processes: practitioners use LCA to obtain the inventory and inject the results into the optimization model. It also shows that most of the LCA studies undertaken on process analysis consider the unit processes as black boxes and build the inventory analysis on fixed operating conditions. Conclusions : The article highlights the interest to better assimilate PSE tools with LCA methodology, in order to produce a more detailed analysis. This will allow optimizing the influence of process operating conditions on environmental impacts and including detailed environmental results into process industry

Evaluation of combined heat and power (CHP) systems using fuzzy shannon entropy and fuzzy TOPSIS

Combined heat and power (CHP) or cogeneration can play a strategic role in addressing environmental issues and climate change. CHP systems require less fuel than separate heat and power systems in order to produce the same amount of energy saving primary energy, improving the security of the supply. Because less fuel is combusted, greenhouse gas emissions and other air pollutants are reduced. If we are to consider the CHP system as "sustainable", we must include in its assessment not only energetic performance but also environmental and economic aspects, presenting a multicriteria issue. The purpose of the paper is to apply a fuzzy multicriteria methodology to the assessment of five CHP commercial technologies. Specifically, the combination of the fuzzy Shannon's entropy and the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach will be tested for this purpose. Shannon's entropy concept, using interval data such as the α-cut, is a particularly suitable technique for assigning weights to criteria — it does not require a decision-making (DM) to assign a weight to the criteria. To rank the proposed alternatives, a fuzzy TOPSIS method has been applied. It is based on the principle that the chosen alternative should be as close as possible to the positive ideal solution and be as far as possible from the negative ideal solution. The proposed approach provides a useful technical–scientific decision-making tool that can effectively support, in a consistent and transparent way, the assessment of various CHP technologies from a sustainable point of view

Modeling and Optimization of M-cresol Isopropylation for Obtaining N-thymol: Combining a Hybrid Artificial Neural Network with a Genetic Algorithm

The application of a hybrid framework based on the combination, artificial neural network-genetic algorithm (ANN-GA), for n-thymol synthesis modeling and optimization has been developed. The effects of molar ratio propylene/cresol (X1), catalyst mass (X2) and temperature (X3) on n-thymol selectivity Y1 and m-cresol conversion Y2 were studied. A 3-8-2 ANN model was found to be very suitable for reaction modeling. The multiobjective optimization, led to optimal operating conditions (0.55 ≤X1≤0.77; 1.773 g ≤ X2 ≤1.86 g; 289.74 °C ≤ X3 ≤291.33 °C) representing good solutions for obtaining high n-thymol selectivity and high m-cresol conversion. This optimal zone corresponded to n-thymol selectivity and m-cresol conversion ranging respectively in the interval [79.3; 79.5]% and [13.4 %; 23.7]%. These results were better than those obtained with a sequential method based on experimental design for which, optimum conditions led to n-thymol selectivity and m-cresol conversion values respectively equal to 67%and 11%. The hybrid method ANN-GA showed its ability to solve complex problems with a good fitting

Biodiesel from microalgae : the use of multi-criteria decision analysis for strain selection

Microalgae strain selection is a vital step in the production of biodiesel from microalgae. In this study, Multi-Criteria Decision Analysis (MCDA) methodologies are adopted to resolve this problem. The aim of this study is to identify the best microalgae strain for viable biodiesel production. The microalgae strains considered here are Heynigia sp., Scenedesmus sp., Niracticinium sp., Chlorella vulgaris, Chlorella sorokiniana and Auxenochlorella protothecoides. The five MCDA methods used to evaluate different strains of microalgae are Analytic Hierarchy Process (AHP), Weighted Sum Method (WSM), Weighted Product Method (WPM), Discrete Compromise Programming (DCP) and Technique for the Order of Preference to the Ideal Solution (TOPSIS). Pairwise comparison matrices are used to determine the weights of the evaluation criteria and it is observed that the most important evaluation criteria are lipid content and growth rate. From the results, Scenedesmus sp. is selected as the best microalgae strain among the six alternatives due to its high lipid content and relatively fast growth rate. The AHP is the most comprehensive of the five MCDA methods because it considers the importance of each criterion and inconsistencies in the rankings are verified. The implementation of the MCDA methods and the results from this study provide an idea of how MCDA can be applied in microalgae strain selection

Capturing Risk in Capital Budgeting

NPS NRP Technical ReportThis proposed research has the goal of proposing novel, reusable, extensible, adaptable, and comprehensive advanced analytical process and Integrated Risk Management to help the (DOD) with risk-based capital budgeting, Monte Carlo risk-simulation, predictive analytics, and stochastic optimization of acquisitions and programs portfolios with multiple competing stakeholders while subject to budgetary, risk, schedule, and strategic constraints. The research covers topics of traditional capital budgeting methodologies used in industry, including the market, cost, and income approaches, and explains how some of these traditional methods can be applied in the DOD by using DOD-centric non-economic, logistic, readiness, capabilities, and requirements variables. Stochastic portfolio optimization with dynamic simulations and investment efficient frontiers will be run for the purposes of selecting the best combination of programs and capabilities is also addressed, as are other alternative methods such as average ranking, risk metrics, lexicographic methods, PROMETHEE, ELECTRE, and others. The results include actionable intelligence developed from an analytically robust case study that senior leadership at the DOD may utilize to make optimal decisions. The main deliverables will be a detailed written research report and presentation brief on the approach of capturing risk and uncertainty in capital budgeting analysis. The report will detail the proposed methodology and applications, as well as a summary case study and examples of how the methodology can be applied.N8 - Integration of Capabilities & ResourcesThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.