Optimization with genetic algorithms of chlorogenic acid's extraction and purification from native potato (Solanum tuberosum L.) peel, using an aqueous two-phase system

Chlorogenic acid is one of the main phenolic components of potatoes (Solanum tuberosum L.). Researchers have demonstrated that this phenolic compound is a higher concentration in the native Huagalina potato peel and is also found in the whole tuber's cooking water. This research aims to extract and purify chlorogenic acid (CGA) from potato Huagalina peel obtained from discarded manufacturing for snacks, using an Aqueous Two-Phase System (ATPS). The central composite design rotatable (CDDR); Response Surface Methods (RSM) in R  x 64 4.0.3 and RSM package was used to identify the regions of interest that resulted in the best extraction levels. The concentration of ethanol (EtOH) and disodium phosphate (DSP) were selected as factors capable of affecting CGA performance. Extraction efficiency values for concentrations were optimized using genetic algorithms (GA) applying the R package GA. . In the present research, a high value of 0.8 was applied, which produced new "offspring" solutions, which share some good characteristics taken from both parents. The mutation is applied after the crossover, altering some genes in the chains, which was 0.1. According to what has been reported, the offspring can replace the entire population or replace individuals with less aptitude. This evaluation cycle and selection-reproduction cycle were repeated until a satisfactory recovery of CGA (97.2%) estimated with GA at a pH of 3.4 (25ºC) was achieved, yielding  443.7 ± 0.062 mg CGA / 100 g dry weight of potato peels.

Rule Optimization of Fuzzy Inference System Sugeno using Evolution Strategy for Electricity Consumption Forecasting

The need for accurate load forecasts will increase in the future because of the dramatic changes occurring in the electricity consumption. Sugeno fuzzy inference system (FIS) can be used for short-term load forecasting. However, challenges in the electrical load forecasting are the data used the data trend. Therefore, it is difficult to develop appropriate fuzzy rules for Sugeno FIS. This paper proposes Evolution Strategy method to determine appropriate rules for Sugeno FIS that have minimum forecasting error. Root Mean Square Error (RMSE) is used to evaluate the goodness of the forecasting result. The numerical experiments show the effectiveness of the proposed optimized Sugeno FIS for several test-case problems. The optimized Sugeno FIS produce lower RMSE comparable to those achieved by other well-known method in the literature

Speed Control Optimization for Autonomous Vehicles with Metaheuristics

International audienceThe development of speed controllers under execution in autonomous vehicles within their dynamic driving task (DDT) is a traditional research area from the point of view of control techniques. In this regard, Proportional-Integral-Derivative (PID) controllers are the most widely used in order to meet the requirements of cruise control. However, fine tuning of the parameters associated with this type of controller can be complex, especially if it is intended to optimize them and reduce their characteristic errors. The objective of the work described in this paper is to evaluate the capacity of several metaheuristics for the adjustment of the parameters Kp, 1/Ti, and 1/Td of a PID controller to regulate the speed of a vehicle. To do this, an adjustment error function has been established from a linear combination of classic estimators of the goodness of the controller, such as overshoot, settling time (ts), steady-state error (ess), and the number of changes of sign of the signal (d). The error obtained when applying the controller has also been compared to a computational model of the vehicle after estimating the parameters Kp, Ki, and Kd, both for a setpoint sequence used in the adjustment of the system parameters and for a sequence not used during the adjustment, and therefore unknown by the system. The main novelty of the paper is to propose a new global error function, a function that enables the use of heuristic optimization methods for PID tuning. This optimization has been carried out by using three methods: genetic algorithms (GA), memetics algorithms (MA), and mesh adaptive direct search (MADS). The results of the application of the optimization methods using the proposed metric show that the accuracy of the PID controller is improved, compared with the classical optimization based on classical methods like the integral absolute error (IAE) or similar metrics, reducing oscillatory behaviours as well as minimizing the analysed performance indexes

Fuzzy FMECA Process Analysis for Managing the Risks in the Lifecycle of a CBCT Scanner

The Failure Mode, Effects, and Criticality Analysis (FMECA) is one of the risk analysis techniques proposed by the ISO 14971 Standard. This analysis allows to identify and assess the consequences of faults that affect each component of a complex system. The FMECA is a forward-type technique used for highlighting critical points and classifying them by priority. It also makes it possible to evaluate the extent of failures by means of numerical indices. It can be applied to a product or to a work process. In the latter case we talk about Process-FMECA. The application of the Process-FMECA to bioengineering is of particular interest because this procedure provides an analysis related to risk management during all the different phases of the medical device life cycle. However, practical applications of this method have revealed some shortcomings that can lead to inaccuracies and inconsistencies regarding the risk analysis and consequent risk prioritization. This paper presents an example of application of a Fuzzy Process-FMECA, an improved Process-FMECA based on fuzzy logic, to a small computerized tomography (CT) device prototype designed for studying the extremities of the human body. This prototype is a CT device that uses the Cone Beam CT (CBCT) technology. The Fuzzy Process-FMECA analysis has made it possible to produce a table of risks, that are quantified according to the specifications of the method. The analysis has shown that each phase or activity is fundamental to guarantee a correct functioning of the device. The methodology applied to this specific device can be paradigmatic for analyzing the process risks for any other medical device

Economic impact failure mode and effects analysis

Failure mode and effects analysis (FMEA) is a method for reducing or eliminating failure modes in a system. A failure mode occurs when a system does not meet its specification. While FMEA is widely used in different industries, its multiple limitations can cause the method to be ineffective. One major limitation is the ambiguity of the risk priority number (RPN), which is used for risk prioritization and is the product of three ordinal variables: severity of effect, probability of occurrence, and likelihood of detection. There have been multiple attempts to address the RPN's ambiguity, but more work is still needed. Any new risk prioritization method needs to have a decision-support system to determine when to implement a corrective action or improvement.This research addresses some of the shortcomings of traditional FMEA through the creation of a new method called Economic Impact FMEA (EI-FMEA). EI-FMEA replaces the three ordinal values used in the RPN calculation with a new set of variables focusing on the expected cost of a failure occurring. A detailed decision-support system allows for the evaluation of corrective actions based on implementation cost, recurring cost, and adjusted failure cost. The RPN risk prioritization metric is replaced by the economic impact value (EIV) risk prioritization metric which ranks risks based on the impact of the corrective action through the largest reduction in potential failure cost. To help with resource allocation, the EIV only ranks risks where the corrective actions are economically sustainable.A comparison of three FMEA methods is performed on a product, and the risk prioritization metrics for each method are used to determine corrective action implementation. An evaluation of the FMEA methods are shown, based on the expected failure cost reduction, using the decision-support criteria of each method.The EI-FMEA method contributes to the body of knowledge by addressing the ambiguity of the RPN in FMEA by creating the EIV risk prioritization metric. This allows the EI-FMEA method to reduce failure cost by providing a decision-support system to determine when to implement a corrective action when both finite and infinite resources are available

Computational intelligence techniques for maximum energy efficiency of cogeneration processes based on internal combustion engines

153 p.El objeto de la tesis consiste en desarrollar estrategias de modelado y optimización del rendimiento energético de plantas de cogeneración basadas en motores de combustión interna (MCI), mediante el uso de las últimas tecnologías de inteligencia computacional. Con esta finalidad se cuenta con datos reales de una planta de cogeneración de energía, propiedad de la compañía EnergyWorks, situada en la localidad de Monzón (provincia de Huesca). La tesis se realiza en el marco de trabajo conjunto del Grupo de Diseño en Electrónica Digital (GDED) de la Universidad del País Vasco UPV/EHU y la empresa Optimitive S.L., empresa dedicada al software avanzado para la mejora en tiempo real de procesos industriale