Fuzzy-based Prioritization of Health, Safety, and Environmental Risks: The Case of a Large Gas Refinery

The main objective of this study was to develop a fuzzy–based framework for the prioritization of health, safety and environment related risks posed against employees, working conditions, and process equipment in large gas refineries. The First Refinery at Pars Special Economic Energy Zone in South of Iran was taken as a case study. For this purpose, health, safety and environment related risks were determined based on the three criteria of impact severity, occurrence probability, and detect-ability using a questionnaire of 33 identified failures. The values obtained were processed by a so-called ‘contribution coefficient’. The results were then subjected to fuzzification and fuzzy rules were defined to calculate the risk level indices as the model outputs, which was then employed to facilitate the management decision-making process by prioritizing the management options. The prioritization values were then classified in six categories in the order of risk severity. Results revealed that failure in a combustion furnace had the highest rank while failure in the slug catcher ranked the lowest among the risk sources. It was also found that about 0.4% of the identified risks prioritized as “intolerable”, 79% as “major”, 20% as “tolerable”, and 0.7% as “minor”. Thus, most of the risks (more than 79%) associated with the refinery has the potential of significant risks. The results indicated that the risk of the pollutant emissions from the combustion furnaces is the highest. Exposures to harmful physical, chemical, psychological, and ergonomic substances are the other risks, respectively

Fuzzy jump wavelet neural network based on rule induction for dynamic nonlinear system identification with real data applications

Aim Fuzzy wavelet neural network (FWNN) has proven to be a promising strategy in the identification of nonlinear systems. The network considers both global and local properties, deals with imprecision present in sensory data, leading to desired precisions. In this paper, we proposed a new FWNN model nominated “Fuzzy Jump Wavelet Neural Network” (FJWNN) for identifying dynamic nonlinear-linear systems, especially in practical applications. Methods The proposed FJWNN is a fuzzy neural network model of the Takagi-Sugeno-Kang type whose consequent part of fuzzy rules is a linear combination of input regressors and dominant wavelet neurons as a sub-jump wavelet neural network. Each fuzzy rule can locally model both linear and nonlinear properties of a system. The linear relationship between the inputs and the output is learned by neurons with linear activation functions, whereas the nonlinear relationship is locally modeled by wavelet neurons. Orthogonal least square (OLS) method and genetic algorithm (GA) are respectively used to purify the wavelets for each sub-JWNN. In this paper, fuzzy rule induction improves the structure of the proposed model leading to less fuzzy rules, inputs of each fuzzy rule and model parameters. The real-world gas furnace and the real electromyographic (EMG) signal modeling problem are employed in our study. In the same vein, piecewise single variable function approximation, nonlinear dynamic system modeling, and Mackey–Glass time series prediction, ratify this method superiority. The proposed FJWNN model is compared with the state-of-the-art models based on some performance indices such as RMSE, RRSE, Rel ERR%, and VAF%. Results The proposed FJWNN model yielded the following results: RRSE (mean±std) of 10e-5±6e-5 for piecewise single-variable function approximation, RMSE (mean±std) of 2.6–4±2.6e-4 for the first nonlinear dynamic system modelling, RRSE (mean±std) of 1.59e-3±0.42e-3 for Mackey–Glass time series prediction, RMSE of 0.3421 for gas furnace modelling and VAF% (mean±std) of 98.24±0.71 for the EMG modelling of all trial signals, indicating a significant enhancement over previous methods. Conclusions The FJWNN demonstrated promising accuracy and generalization while moderating network complexity. This improvement is due to applying main useful wavelets in combination with linear regressors and using fuzzy rule induction. Compared to the state-of-the-art models, the proposed FJWNN yielded better performance and, therefore, can be considered a novel tool for nonlinear system identificationPeer ReviewedPostprint (published version

Review of medical image classification using the adaptive neuro-fuzzy inference system

Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. A brief comparison with other classifiers, main advantages and drawbacks of this classifier are investigated

Fuzzy-based Prioritization of Health, Safety, and Environmental Risks: The Case of a Large Gas Refinery

The main objective of this study was to develop a fuzzy–based framework for the prioritization of health, safety and environment related risks posed against employees, working conditions, and process equipment in large gas refineries. The First Refinery at Pars Special Economic Energy Zone in South of Iran was taken as a case study. For this purpose, health, safety and environment related risks were determined based on the three criteria of impact severity, occurrence probability, and detect-ability using a questionnaire of 33 identified failures. The values obtained were processed by a so-called ‘contribution coefficient’. The results were then subjected to fuzzification and fuzzy rules were defined to calculate the risk level indices as the model outputs, which was then employed to facilitate the management decision-making process by prioritizing the management options. The prioritization values were then classified in six categories in the order of risk severity. Results revealed that failure in a combustion furnace had the highest rank while failure in the slug catcher ranked the lowest among the risk sources. It was also found that about 0.4% of the identified risks prioritized as “intolerable”, 79% as “major”, 20% as “tolerable”, and 0.7% as “minor”. Thus, most of the risks (more than 79%) associated with the refinery has the potential of significant risks. The results indicated that the risk of the pollutant emissions from the combustion furnaces is the highest. Exposures to harmful physical, chemical, psychological, and ergonomic substances are the other risks, respectively

Regulation of blood glucose concentration in type 1 diabetics using single order sliding mode control combined with fuzzy on-line tunable gain, a simulation study

Diabetes is considered as a global affecting disease with an increasing contribution to both mortality rate and cost damage in the society. Therefore, tight control of blood glucose levels has gained significant attention over the decades. This paper proposes a method for blood glucose level regulation in type 1 diabetics. The control strategy is based on combining the fuzzy logic theory and single order sliding mode control (SOSMC) to improve the properties of sliding mode control method and to alleviate its drawbacks. The aim of the proposed controller that is called SOSMC combined with fuzzy on-line tunable gain is to tune the gain of the controller adaptively. This merit causes a less amount of control effort, which is the rate of insulin delivered to the patient body. As a result, this method can decline the risk of hypoglycemia, a lethal phenomenon in regulating blood glucose level in diabetics caused by a low blood glucose level. Moreover, it attenuates the chattering observed in SOSMC significantly. It is worth noting that in this approach, a mathematical model called minimal model is applied instead of the intravenously infused insulin-blood glucose dynamics. The simulation results demonstrate a good performance of the proposed controller in meal disturbance rejection and robustness against parameter changes. In addition, this method is compared to fuzzy high-order sliding mode control (FHOSMC) and the superiority of the new method compared to FHOSMC is shown in the results

Predictive Control of the Blood Glucose Level in Type I Diabetic Patient Using Delay Differential Equation Wang Model

Because of increasing risk of diabetes, the measurement along with control of blood sugar has been of great importance in recent decades. In type I diabetes, because of the lack of insulin secretion, the cells cannot absorb glucose leading to low level of glucose. To control blood glucose (BG), the insulin must be injected to the body. This paper proposes a method for BG level regulation in type I diabetes. The control strategy is based on nonlinear model predictive control. The aim of the proposed controller optimized with genetics algorithms is to measure BG level each time and predict it for the next time interval. This merit causes a less amount of control effort, which is the rate of insulin delivered to the patient body. Consequently, this method can decrease the risk of hypoglycemia, a lethal phenomenon in regulating BG level in diabetes caused by a low BG level. Two delay differential equation models, namely Wang model and Enhanced Wang model, are applied as controller model and plant, respectively. The simulation results exhibit an acceptable performance of the proposed controller in meal disturbance rejection and robustness against parameter changes. As a result, if the nutrition of the person decreases instantly, the hypoglycemia will not happen. Furthermore, comparing this method with other works, it was shown that the new method outperforms previous studies

پیش‌بینی نمای قوس ساجیتال کفش‌های غلتکی بر اساس کینماتیک مچ پا حین راه رفتن: رویکرد شبکه عصبی مصنوعی

Introduction: Sagittal rocker profiles are one of the most commonly prescribed therapeutic footwear interventions to alter or adapt lower limb joints’ kinematics and kinetics. However, the prescription criteria for rocker profiles are commonly based on theoretical considerations. Thus, conducting experimental studies and experiment and error may result in their better prescription and use. A complementary approach is to use intelligent technology to predict curve profile to suit a specific joint position. The aim of this study was to predict sagittal curve profile of the rollover footwear from ankle kinematics while walking by applying an artificial neural network (ANN). Materials and Methods: In the present study, 20 healthy participants (with mean age of 33.1 years) walked on a straight path for 10 meters wearing two different shoes with two different sole curved profiles and ankle kinematic data were collected using reflective markers. The ANN was trained to associate set of ankle sagittal plane motions during stance phase with outsole curve profiles, and then, predict the latter based on the former. The ANN was trained using the data from 13 participants (control group) to obtain the model and the data from the remaining participants (intervention group) was used for the validation of the study purposes. Results: The achieved accuracy was very satisfactory, since the correlation coefficients between the predicted output and the actual curve profile in the validation data were higher than 0.95 for both types of rollover footwear. Conclusion: In this study, a novel algorithm was proposed for sole curve profile characterization of rollover footwear using an ANN model. The results of this study may be useful to designers of footwear, lower limb prostheses, orthoses, and walking casts/boots