Freeze-drying modeling and monitoring using a new neuro-evolutive technique

This paper is focused on the design of a black-box model for the process of freeze-drying of pharmaceuticals. A new methodology based on a self-adaptive differential evolution scheme is combined with a back-propagation algorithm, as local search method, for the simultaneous structural and parametric optimization of the model represented by a neural network. Using the model of the freeze-drying process, both the temperature and the residual ice content in the product vs. time can be determine off-line, given the values of the operating conditions (the temperature of the heating shelf and the pressure in the drying chamber). This makes possible to understand if the maximum temperature allowed by the product is trespassed and when the sublimation drying is complete, thus providing a valuable tool for recipe design and optimization. Besides, the black box model can be applied to monitor the freeze-drying process: in this case, the measurement of product temperature is used as input variable of the neural network in order to provide in-line estimation of the state of the product (temperature and residual amount of ice). Various examples are presented and discussed, thus pointing out the strength of the too

Aplicațiile inteligenței artificiale în oftalmologie

Department of Ophtalmology, “Grigore T. Popa” University of Medicine and Pharmacy, Iasi,Romania, Gheorghe Asachi” Technical University of Iasi, Faculty of Chemical Engineering and Environmental Protection 11 “Cristofor- Simionescu”, Department of Chemical EngineeringRezumat. Instrumentele inteligenței artificiale și, îndeosebi, rețelele neuronale artificiale, sunt tot mai des implicate în diagnosticul și managementul personalizat al bolilor oftalmologice. Imaginile OCT sunt utilizate pentru diagnosticul precoce, monitorizarea și managementul bolilor retinei, cum ar fi edem macular diabetic (EMD) și degenerescență maculară legată de vârstă (DMLV). Citirea automată a OCT a avut rezultate promițătoare în EMD și în identificarea formelor exudative ale DMLV. Cea mai frecventă utilizare a rețelelor neuronale în oftalmologie a fost în stabilirea precoce a diagnosticului de glaucom, atunci când sunt dubii de diagnostic. Rețelele neuronale au avut un rol important în stabilirea necesităţii iniţierii terapiei precoce antiglaucomatoase pentru a preveni progresia bolii. Numeroase studii din literatura de specialitate demonstrează folosirea cu succes a acestor instrumente ale inteligenței artificiale în oftalmologie, pe direcții cum ar fi: evaluarea câmpului vizual, a nervului optic, a stratului fibrelor nervoase retiniene, oferind astfel o mai bună precizie în identificarea progresiei în glaucom şi a modificărilor retiniene în diabet. În oftalmologie, Inteligenta artificiala are potențialul de a crește accesul pacientului la screening / diagnostic clinic și la scăderea costurilor enorme solicitate de asistența medicală, mai ales atunci când riscul apariției bolii este ridicat sau comunitățile se confruntă cu resurse financiare reduse. Reţelele neuronale artificiale sunt utile în stabilirea diagnosticului diferitelor boli, însă informaţiile obţinute au rolul de a ajuta decizia finală care va fi luată de clinician, dar nu va înlocui rolul acestuia.Artificial intelligence tools, and especially artificial neural networks, are increasingly involved in the diagnosis and personalized management of ophthalmic diseases. OCT images are used for early diagnosis, monitoring and management of retinal diseases such as diabetic macular edema (EMD) and age-related macular degeneration (DMLV). Automatic reading of OCT has had promising results in EMD and in identifying exudative forms of DMLV. The most common use of neural networks in ophthalmology has been in early diagnosis of glaucoma, when there is doubt about the diagnosis. Neural networks have played an important role in determining the need for early antiglaucoma therapy to prevent disease progression. Numerous studies in the literature demonstrate the successful use of these tools of artificial intelligence in ophthalmology, in directions such as: assessment of visual field, optic nerve, retinal nerve fiber layer, thus providing better accuracy in identifying progression in glaucoma. and retinal changes in diabetes. Conclusions. In ophthalmology, Artificial Intelligence has the potential to increase patient access to screening / clinical diagnosis and to reduce the enormous costs required by healthcare, especially when the risk of disease is high or communities face limited financial resources. Artificial neural networks are useful in diagnosing various diseases, but the information obtained is intended to help the final decision that will be made by the clinician, but will not replace his role

Experimental analysis and mathematical prediction of Cd(II) removal by biosorption using support vector machines and genetic algorithms

We investigated the bioremoval of Cd(II) in batch mode, using dead and living biomass of Trichoderma viride. Kinetic studies revealed three distinct stages of the biosorption process. The pseudo-second order model and the Langmuir model described well the kinetics and equilibrium of the biosorption process, with a determination coefficient, R2 > 0.99. The value of the mean free energy of adsorption, E, is less than 16 kJ/mol at 25°C, suggesting that, at low temperature, the dominant process involved in Cd(II) biosorption by dead T. viride is the chemical ion-exchange. With the temperature increasing to 4050°C, E values are above 16 kJ/mol, showing that the particle diffusion mechanism could play an important role in Cd(II) biosorption. The studies on T. viride growth in Cd(II) solutions and its bioaccumulation performance showed that the living biomass was able to bioaccumulate 100% Cd(II) from a 50 mg/L solution at pH 6.0. The influence of pH, biomass dosage, metal concentration, contact time and temperature on the bioremoval efficiency was evaluated to further assess the biosorption capability of the dead biosorbent. These complex influences were correlated by means of a modeling procedure consisting in data driven approach in which the principles of artificial intelligence were applied with the help of support vector machines (SVM), combined with genetic algorithms (GA). According to our data, the optimal working conditions for the removal of 98.91% Cd(II) by T. viride were found for an aqueous solution containing 26.11 mg/L Cd(II) as follows: pH 6.0, contact time of 3833 min, 8 g/L biosorbent, temperature 46.5°C. The complete characterization of bioremoval parameters indicates that T. viride is an excellent material to treat wastewater containing low concentrations of metal

Performance Comparison of Different Regression Methods for a Polymerization Process with Adaptive Sampling

Developing complete mechanistic models for polymerization reactors is not easy, because complex reactions occur simultaneously; there is a large number of kinetic parameters involved and sometimes the chemical and physical phenomena for mixtures involving polymers are poorly understood. To overcome these difficulties, empirical models based on sampled data can be used instead, namely regression methods typical of machine learning field. They have the ability to learn the trends of a process without any knowledge about its particular physical and chemical laws. Therefore, they are useful for modeling complex processes, such as the free radical polymerization of methyl methacrylate achieved in a batch bulk process. The goal is to generate accurate predictions of monomer conversion, numerical average molecular weight and gravimetrical average molecular weight. This process is associated with non-linear gel and glass effects. For this purpose, an adaptive sampling technique is presented, which can select more samples around the regions where the values have a higher variation. Several machine learning methods are used for the modeling and their performance is compared: support vector machines, k-nearest neighbor, k-nearest neighbor and random forest, as well as an original algorithm, large margin nearest neighbor regression. The suggested method provides very good results compared to the other well-known regression algorithms

Machine Learning Methods Applied for Modeling the Process of Obtaining Bricks Using Silicon-Based Materials

Most of the time, industrial brick manufacture facilities are designed and commissioned for a particular type of manufacture mix and a particular type of burning process. Productivity and product quality maintenance and improvement is a challenge for process engineers. Our paper aims at using machine learning methods to evaluate the impact of adding new auxiliary materials on the amount of exhaust emissions. Experimental determinations made in similar conditions enabled us to build a database containing information about 121 brick batches. Various models (artificial neural networks and regression algorithms) were designed to make predictions about exhaust emission changes when auxiliary materials are introduced into the manufacture mix. The best models were feed-forward neural networks with two hidden layers, having MSE 2 > 0.82 and, as regression model, kNN with error < 0.6. Also, an optimization procedure, including the best models, was developed in order to determine the optimal values for the parameters that assure the minimum quantities for the gas emission. The Pareto front obtained in the multi-objective optimization conducted with grid search method allows the user the chose the most convenient values for the dry product mass, clay, ash and organic raw materials which minimize gas emissions with energy potential

A Hybrid Competitive Evolutionary Neural Network Optimization Algorithm for a Regression Problem in Chemical Engineering

Neural networks have demonstrated their usefulness for solving complex regression problems in circumstances where alternative methods do not provide satisfactory results. Finding a good neural network model is a time-consuming task that involves searching through a complex multidimensional hyperparameter and weight space in order to find the values that provide optimal convergence. We propose a novel neural network optimizer that leverages the advantages of both an improved evolutionary competitive algorithm and gradient-based backpropagation. The method consists of a modified, hybrid variant of the Imperialist Competitive Algorithm (ICA). We analyze multiple strategies for initialization, assimilation, revolution, and competition, in order to find the combination of ICA steps that provides optimal convergence and enhance the algorithm by incorporating a backpropagation step in the ICA loop, which, together with a self-adaptive hyperparameter adjustment strategy, significantly improves on the original algorithm. The resulting hybrid method is used to optimize a neural network to solve a complex problem in the field of chemical engineering: the synthesis and swelling behavior of the semi- and interpenetrated multicomponent crosslinked structures of hydrogels, with the goal of predicting the yield in a crosslinked polymer and the swelling degree based on several reaction-related input parameters. We show that our approach has better performance than other biologically inspired optimization algorithms and generates regression models capable of making predictions that are better correlated with the desired outputs

Ensembles of Biologically Inspired Optimization Algorithms for Training Multilayer Perceptron Neural Networks

Artificial neural networks have proven to be effective in a wide range of fields, providing solutions to various problems. Training artificial neural networks using evolutionary algorithms is known as neuroevolution. The idea of finding not only the optimal weights and biases of a neural network but also its architecture has drawn the attention of many researchers. In this paper, we use different biologically inspired optimization algorithms to train multilayer perceptron neural networks for generating regression models. Specifically, our contribution involves analyzing and finding a strategy for combining several algorithms into a hybrid ensemble optimizer, which we apply for the optimization of a fully connected neural network. The goal is to obtain good regression models for studying and making predictions for the process of free radical polymerization of methyl methacrylate (MMA). In the first step, we use a search procedure to find the best parameter values for seven biologically inspired optimization algorithms. In the second step, we use a subset of the best-performing algorithms and improve the search capability by combining the chosen algorithms into an ensemble of optimizers. We propose three ensemble strategies that do not involve changes in the logic of optimization algorithms: hybrid cascade, hybrid single elite solution, and hybrid multiple elite solutions. The proposed strategies inherit the advantages of each individual optimizer and have faster convergence at a computational effort very similar to an individual optimizer. Our experimental results show that the hybrid multiple elite strategy ultimately produces neural networks which constitute the most dependable regression models for the aforementioned process