Neural-Networks-Based Feedback Linearization versus Model Predictive Control of Continuous Alcoholic Fermentation Process

In this work advanced nonlinear neural networks based control system design algorithms are adopted to control a mechanistic model for an ethanol fermentation process. The process model equations for such systems are highly nonlinear. A neural network strategy has been implemented in this work for capturing the dynamics of the mechanistic model for the fermentation process. The neural network achieved has been validated against the mechanistic model. Two neural network based nonlinear control strategies have also been adopted using the model identified. The performance of the feedback linearization technique was compared to neural network model predictive control in terms of stability and set point tracking capabilities. Under servo conditions, the feedback linearization algorithm gave comparable tracking and stability. The feedback linearization controller achieved the control target faster than the model predictive one but with vigorous and sudden controller moves.Wiley Online Librar

Treatment of waters colored with methylene blue dye by vacuum membrane distillation

Textile industries consume large amounts of water and generate highly dye-contaminated effluents. Textile wastewaters have to be treated in order to be recycled in the process or to meet legislative requirements before being discharged. The objective of the present study was to examine the potential use of the vacuum membrane distillation process for the treatment of dyed solutions. Methylene blue (MB) was used as a model dye. Batch experiments were conducted on dilute MB-water mixtures using a tubular polypropylene membrane module. The concentration of MB dye within the feed reservoir was monitored over time. The impact of operating variables such as feed temperature, flow rate and initial dye concentration was investigated. A mathematical model incorporating temperature and concentration polarization effects was developed and validated on the experimental data

Representation of Adsorption Data for the Isopropanol-Water System using Neural Network Techniques

Molecular sieves and palm stone, a newly developed bio-based adsorbent, were used to break an azeotropic isopropanol-water system via an adsorptive distillation process. Equilibrium data at different inlet water contents are presented. The data were obtained with a fixed bed adsorptive distillation process using Type 3A and Type 4A molecular sieves and palm stone. An artificial neural network (ANN) technique was used to represent the isotherm equilibrium data of this azeotropic system. The ANN prediction results were compared with the Guggenheim-Anderson-de Boer (GAB) isotherm model. It was possible to break the isopropanol-water azeotrope using this separation process with the adsorbents used in this work. Water uptake increases as the water content in the feed decreases from 16?% to 10?%. Although the GAB isotherm model was found to be applicable to the water vapor sorption data on the adsorbents examined, the ANN model fitted the equilibrium data more efficiently

Bench-scale and packed bed sorption of methylene blue using treated olive pomace and charcoal

A combination of olive pomace after solvent extraction and charcoal produced from the solid waste of olive oil press industry was used as an adsorbent for the removal of methylene blue (MB) dye from aqueous solutions. Batch tests showed that up to 80% of dye was removed when the dye concentration was 10 mg/ml and the sorbent concentration was 45 mg/ml. An increase in the olive pomace concentration resulted in greater dye removal from aqueous solution, and an increase in MB dye concentration at constant adsorbent concentration increased the dye loading per unit weigh of adsorbent. In the kinetic of the adsorbent process, the adsorption data followed the second-order kinetic model better than first order kinetic model. Charcoal showed higher sorption capacity (uptake) than that of olive pomace. In the fixed bed adsorption experiment, the breakthrough curves showed constant pattern behavior, typical of favorable isotherms. The breakthrough time increased with increasing bed height, decreasing flow rate and decreasing influent concentration and methylene blue dye uptake. The uptake of MB dye was significantly increased when a mixture of olive pomace and charcoal was packed in the column in a multi-layer fashion. Different models were used to describe the behavior of this packed-sorption process

A novel technique of paper mill sludge conversion to bioethanol toward sustainable energy production: Effect of fiber recovery on the saccharification hydrolysis and fermentation

A new process for the production of bioethanol from paper mill sludge (PMS) is described in this work. PMS biomass feedstock was processed via the simultaneous saccharification and fermentation (SSF) with and without accelerants. The enzymatic hydrolysis and fermentation were first evaluated, and the energy demand was 2.2 MJ/L of produced ethanol. When the enzymatic hydrolysis and fermentation were combined, the energy demand was reduced to 1.0 MJ/L ethanol, the sugars production increased, and the overall capital cost of the process decreased. The sugar yield was improved by adding accelerant and selecting the optimal fiber recovery method. The accelerant improved the enzymatic hydrolysis via a pathing/bridging mechanism. The SSF with the chemical fiber recovery method coupled with accelerant addition would be the best process configuration. Upon this combination, the glucose profile was enhanced from 9.8 g/L to 17.0 g/L. The sludge fiber conversion by SSF was improved by selecting an efficient fiber recovery method combined with the accelerant addition. SSF in chemical fiber recovery with accelerant addition was the best process by a 10% improvement of ethanol yield. The proposed process configuration offers a lower cost and sustainable process and contributes to the circular economy of zero waste discharges.Scopu