Using neural networks to obtain indirect information about the state variables in an alcoholic fermentation process

This work provides a manual design space exploration regarding the structure, type, and inputs of a multilayer neural network (NN) to obtain indirect information about the state variables in the alcoholic fermentation process. The main benefit of our application is to help experts reduce the time needed for making the relevant measurements and to increase the lifecycles of sensors in bioreactors. The novelty of this research is the flexibility of the developed application, the use of a great number of variables, and the comparative presentation of the results obtained with different NNs (feedback vs. feed-forward) and different learning algorithms (Back-Propagation vs. Levenberg–Marquardt). The simulation results show that the feedback neural network outperformed the feed-forward neural network. The NN configuration is relatively flexible (with hidden layers and a number of nodes on each of them), but the number of input and output nodes depends on the fermentation process parameters. After laborious simulations, we determined that using pH and CO2 as inputs reduces the prediction errors of the NN. Thus, besides the most commonly used process parameters like fermentation temperature, time, the initial concentration of the substrate, the substrate concentration, and the biomass concentration, by adding pH and CO2, we obtained the optimum number of input nodes for the network. The optimal configuration in our case was obtained after 1500 iterations using a NN with one hidden layer and 12 neurons on it, seven neurons on the input layer, and one neuron as the output. If properly trained and validated, this model can be used in future research to accurately predict steady-state and dynamic alcoholic fermentation process behaviour and thereby improve process control performance

Modelling of Biotechnological Processes - An approach based on Artificial Neural Networks

In this chapter we describe a software tool for modelling fermentation processes, the FerMoANN, which allows researchers in biology and biotechnology areas to access the potential of Artificial Neural Networks (ANNs) for this task. The FerMoANN is tested and validated using two fermentation processes, an Escherichia coli recombinant protein production and the production of a secreted protein with Saccharomyces cerevisiae in fed-batch reactors. The application to these two case studies, tested for different configurations of feedforward ANNs, illustrate the usefulness of these structures, when trained according to a supervised learning paradigm

CESE-2019

This book is a collation of articles published in the Special Issue "CESE-2019: Applications of Membranes" in the journal Sustainability. It contains a wide variety of topics such as the removal of trace organic contaminants using combined direct contact membrane distillation–UV photolysis; evaluating the feasibility of forward osmosis in diluting reverse osmosis concentrate; tailoring the effects of titanium dioxide (TiO2) and polyvinyl alcohol (PVA) in the separation and antifouling performance of thin-film composite polyvinylidene fluoride (PVDF) membrane; enhancing the antibacterial properties of PVDF membrane by surface modification using TiO2 and silver nanoparticles; and reviews on membrane fouling in membrane bioreactor (MBR) systems and recent advances in the prediction of fouling in MBRs. The book is suitable for postgraduate students and researchers working in the field of membrane applications for treating aqueous solutions

Modeling water flux in osmotic membrane bioreactor by adaptive network-based fuzzy inference system and artificial neural network.

Osmotic Membrane Bioreactor (OMBR) is an emerging technology for wastewater treatment with membrane fouling as a major challenge. This study aims to develop Adaptive Network-based Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) models in simulating and predicting water flux in OMBR. Mixed liquor suspended solid (MLSS), electrical conductivity (EC) and dissolved oxygen (DO) were used as model inputs. Good prediction was demonstrated by both ANFIS models with R2 of 0.9755 and 0.9861, and ANN models with R2 of 0.9404 and 0.9817, for thin film composite (TFC) and cellulose triacetate (CTA) membranes, respectively. The root mean square error for TFC (0.2527) and CTA (0.1230) in ANFIS models was lower than in ANN models at 0.4049 and 0.1449. Sensitivity analysis showed that EC was the most important factor for both TFC and CTA membranes in ANN models, while EC (TFC) and MLSS (CTA) are key parameters in ANFIS models

Performance comparison of SVM and ANN for aerobic granular sludge

To comply with growing demand for high effluent quality of Domestic Wastewater Treatment Plant (WWTP), a simple and reliable prediction model is thus needed. The wastewater treatment technology considered in this paper is an Aerobic Granular Sludge (AGS). The AGS systems are fundamentally complex due to uncertainty and non-linearity of the system makes it hard to predict. This paper presents model predictions and optimization as a tool in predicting the performance of the AGS. The input-output data used in model prediction are (COD, TN, TP, AN, and MLSS). After feature analysis, the prediction of the models using Support Vector Machine (SVM) and Feed-Forward Neural Network (FFNN) are developed and compared. The simulation of the model uses the experimental data obtained from Sequencing Batch Reactor under hot temperature of 50˚C. The simulation results indicated that the SVM is preferable to FFNN and it can provide a useful tool in predicting the effluent quality of WWTP

Partial Least Squares and Neural Network Based Identification of Process Dynamics & Design of Neural Controllers

Present study implemented the Neural network (NN) and Partial least squares (PLS) based identification of process dynamics for single-input single output (SISO) as well as multi-input multi-output (MIMO)systems. In the present study, the Neural network (NN) based controller design has been implemented for a non-linear continuous bioreactor process. Multilayer feed forward networks (FFNN) were used as direct inverse neural network (DINN) controllers as well as IMC based NN controllers. The training as well as testing database was created by perturbing the open loop process with pseudo random signals (PRS).DINN controllers performed effectively for set-point tracking. To address the disturbance rejection problems, which are very likely to be faced by the bioreactors, the IMC based neural control architecture was proposed with suitable choice of filter and disturbance transfer function. To assess the controllability of the various configurations, like conventional turbidostat and nutristat& concentration turbidostat and nutristat, the offset or degree of disturbance rejection by the proposed IMC based NN controllers were utilized. The ‘concentration turbidostat’using the feed substrate concentration as the manipulated variable was found to be the best control configuration among the continuous bioreactor configurations.A (2×2) distillation column was simulated to generate the time series data consisting various inputs and outputs of the process. Multivariate statistical technique PLS was used to relate the scores of input and output matrices. ARX as well as linear least squares techniques were used for inner relation development between the input-output scores. The PLS model of the distillation column dynamics could simulate the process with reasonable accuracy

Machine Learning Based Applications for Data Visualization, Modeling, Control, and Optimization for Chemical and Biological Systems

This dissertation report covers Yan Ma’s Ph.D. research with applicational studies of machine learning in manufacturing and biological systems. The research work mainly focuses on reaction modeling, optimization, and control using a deep learning-based approaches, and the work mainly concentrates on deep reinforcement learning (DRL). Yan Ma’s research also involves with data mining with bioinformatics. Large-scale data obtained in RNA-seq is analyzed using non-linear dimensionality reduction with Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP), followed by clustering analysis using k-Means and Hierarchical Density-Based Spatial Clustering with Noise (HDBSCAN). This report focuses on 3 case studies with DRL optimization control including a polymerization reaction control with deep reinforcement learning, a bioreactor optimization, and a fed-batch reaction optimization from a reactor at Dow Inc.. In the first study, a data-driven controller based on DRL is developed for a fed-batch polymerization reaction with multiple continuous manipulative variables with continuous control. The second case study is the modeling and optimization of a bioreactor. In this study, a data-driven reaction model is developed using Artificial Neural Network (ANN) to simulate the growth curve and bio-product accumulation of cyanobacteria Plectonema. Then a DRL control agent that optimizes the daily nutrient input is applied to maximize the yield of valuable bio-product C-phycocyanin. C-phycocyanin yield is increased by 52.1% compared to a control group with the same total nutrient content in experimental validation. The third case study is employing the data-driven control scheme for optimization of a reactor from Dow Inc, where a DRL-based optimization framework is established for the optimization of the Multi-Input, Multi-Output (MIMO) reaction system with reaction surrogate modeling. Yan Ma’s research overall shows promising directions for employing the emerging technologies of data-driven methods and deep learning in the field of manufacturing and biological systems. It is demonstrated that DRL is an efficient algorithm in the study of three different reaction systems with both stochastic and deterministic policies. Also, the use of data-driven models in reaction simulation also shows promising results with the non-linear nature and fast computational speed of the neural network models