Intermolecular And Surface Forces

Preface. Automatic Train Operation System by Predictive Fuzzy Control (S. Yasunobu, S. Miyamoto). Application of Fuzzy Reasoning to the Water Purification Process (O. Yagashita, O. Itoh, M. Sugeno). The Application of a Fuzzy Controller to the Control of a Multi-Degree-of-Freedom Robot Arm (E.M. Scharf, N.J. Mandic). Optimizing Control of a Diesel Engine (Y. Murayama et al.). Development of Performance Adaptive Fuzzy Controllers with Application to Continuous Casting Plants (G. Bartolini et al.). A Fuzzy Logic Controller for Aircraft Flight Control (L.I. Larkin). Automobile Speed Control System Using a Fuzzy Logic Controller (S. Murakami, M. Maeda). An Experimental Study on Fuzzy Parking Control Using a Model Car (M. Sugeno, K. Murakami). A Fuzzy Controller in Turning Process Automation (Y. Sakai, K. Ohkusa). Design of Fuzzy Control Algorithms with the Aid of Fuzzy Models (W. Pedrycz). Human Operator's Fuzzy Model in Man-Machine System with a Nonlinear Controlled Object (K. Matsushima, H. Sugiyama). The Influence of Some Parameters on the Accuracy of a Fuzzy Model (J.B. Kiszka, M.E. Kochanska, D.S. Sliwinska). A Microprocessor Based Fuzzy Controller for Industrial Purposes (T. Yamazaki, M. Sugeno). The Application of Fuzzy and Artificial Intelligence Methods in the Building of a Blast Furnace Smelting Process Model (H. Zhao, M. Ma). An Annotated Bibliography of Fuzzy Control (R.M. Tong).

Industrial applications of fuzzy control

As the chronicle of plate motions through time, paleogeography is fundamental to our understanding of plate tectonics and its role in shaping the geology of the present-day. To properly appreciate the history of tectonics—and its influence on the deep Earth and climate—it is imperative to seek an accurate and global model of paleogeography. However, owing to the incessant loss of oceanic lithosphere through subduction, the paleogeographic reconstruction of ‘full-plates’ (including oceanic lithosphere) becomes increasingly challenging with age. Prior to 150 Ma ∼60% of the lithosphere is missing and reconstructions are developed without explicit regard for oceanic lithosphere or plate tectonic principles; in effect, reflecting the earlier mobilistic paradigm of continental drift. Although these ‘continental’ reconstructions have been immensely useful, the next-generation of mantle models requires global plate kinematic descriptions with full-plate reconstructions. Moreover, in disregarding (or only loosely applying) plate tectonic rules, continental reconstructions fail to take advantage of a wealth of additional information in the form of practical constraints. Following a series of new developments, both in geodynamic theory and analytical tools, it is now feasible to construct full-plate models that lend themselves to testing by the wider Earth-science community. Such a model is presented here for the late Paleozoic (410–250 Ma) together with a review of the underlying data. Although we expect this model to be particularly useful for numerical mantle modeling, we hope that it will also serve as a general framework for understanding late Paleozoic tectonics, one on which future improvements can be built and further tested.

Plate Tectonics in the Late Paleozoic

This paper first reviews different methods of designing thermal error models, before concentrating on employing ANFIS models.The GM(1, N) model and fuzzy c-means clustering are used for variable selection, which is capable of simplifying the system prediction model.The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. Thermal errors can have significant effects on CNC machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This paper first reviews different methods of designing thermal error models, before concentrating on employing an adaptive neuro fuzzy inference system (ANFIS) to design two thermal prediction models: ANFIS by dividing the data space into rectangular sub-spaces (ANFIS-Grid model) and ANFIS by using the fuzzy c-means clustering method (ANFIS-FCM model). Grey system theory is used to obtain the influence ranking of all possible temperature sensors on the thermal response of the machine structure. All the influence weightings of the thermal sensors are clustered into groups using the fuzzy c-means (FCM) clustering method, the groups then being further reduced by correlation analysis.A study of a small CNC milling machine is used to provide training data for the proposed models and then to provide independent testing data sets. The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. The residual value of the proposed model is smaller than ?4µm. This combined methodology can provide improved accuracy and robustness of a thermal error compensation system.

The application of ANFIS prediction models for thermal error compensation on CNC machine tools

Advancing CO2 enhanced oil recovery and storage in unconventional oil play—Experimental studies on Bakken shales

Optimum design of CO2 storage and oil recovery under geological uncertainty

This article presents numerical simulations of CO2 storage mechanisms in the Pennsylvanian Upper Morrow sandstone reservoir, locally termed the Morrow B sandstone in the Farnsworth Unit (FWU) of Ochiltree County, Texas. The CO2 storage mechanisms considered in the study under a CO2 enhanced oil recovery (EOR) mode include structural-stratigraphic trapping, CO2 dissolution in formation water and oil, and residual trapping. The reservoir simulation model was constructed on the basis of field geophysical, geological, and engineering data such as three-dimensional surface seismic data, well logs, and fluid analysis. A representative fluid sampled from the reservoir was analyzed and used to tune the equation of state. A thermodynamic minimum miscible pressure was subsequently computed and compared to the experimental outcome. A history-matched model was constructed and used as a baseline to determine the effects of different hypothetical injection strategies (that consider CO2 purchase, gas recycling, and infi...

Martha Cather

Papers

Optimum design of CO2 storage and oil recovery under geological uncertainty

Evaluation of CO2 Storage Mechanisms in CO2 Enhanced Oil Recovery Sites: Application to Morrow Sandstone Reservoir

Predicting injection profiles using ANFIS

Machine learning based co-optimization of carbon dioxide sequestration and oil recovery in CO2-EOR project

An Improved Approach for Sandstone Reservoir Characterization