A review on intelligent sensory modelling

Sensory evaluation plays an important role in the quality control of food productions. Sensory data obtained through sensory evaluation are generally subjective, vague and uncertain. Classically, factorial multivariate methods such as Principle Component Analysis (PCA), Partial Least Square (PLS) method, Multiple Regression (MLR) method and Response Surface Method (RSM) are the common tools used to analyse sensory data. These methods can model some of the sensory data but may not be robust enough to analyse nonlinear data. In these situations, intelligent modelling techniques such as Fuzzy Logic and Artificial neural network (ANNs) emerged to solve the vagueness and uncertainty of sensory data. This paper outlines literature of intelligent sensory modelling on sensory data analysis

Performance analysis of a solar heat collector through experimental and CFD investigation

In order to attain maximum efficiency in a solar drying system, continuous effort is made to the key component of solar dryer – solar heat collector (SHC). This research aimed to evaluate the thermal performance of SHC with different flow configuration in the air passage, namely single-pass (S-SHC) and multiple-pass (M-SHC), under natural convection (average air velocity = 0.2 m/s). In order to study the flow and heat transfer characteristics across the SHC, performance analysis was carried out by Computational Fluid Dynamic (CFD) simulation and later validated by experimental results. From the simulation model, the collector outlet temperature and efficiency of M-SHC at maximum solar radiation were 67.4 °C and 10.04%, respectively with percentage error of 8.6% and 17.79% to the experimental results. The presence of recirculation region indicated extended drying air residence time in the M-SHC, resulting in high temperature growth from 8.8% to 12.1% across the air passage compared to S-SHC. In addition, heat transfer enhancement in M-SHC was achieved by compensating radiation heat loss observed in S-SHC through the modification of airflow configuration. Both experimental and theoretical analysis in this study showed that the proposed enhancement significantly improved the performance of SHC having air passage made from recycled aluminum cans

Optimization of yeast fermentation process using genetic algorithm

This paper proposes genetic algorithm (GA) to optimize the productivity of yeast fermentation process. The proposed optimizer aims to maximize yeast productivity while minimizing the by-product of ethanol. Various initial glucose concentrations will affect yeast productivity and influence the performance of yeast fermentation. Yeast has relatively high ethanol production as compared with other microorganisms. Since the excessive ethanol formation in the yeast fermentation process will have a negative impact on quality of the product, it is needed to optimize glucose feeding rate at optimal level for maximizing the yeast productivity. The conventional open-loop feeding system is inadequate to minimize the growth of by-product as the system will not regulate the glucose feeding rate based on the instant needs. Thus, GA is proposed to optimize the glucose feeding profile based on the instant concentration of yeast, glucose, oxygen and ethanol inside the fermentation tank. The results show the proposed GA can obtain a higher yield production of 95.3% as compared to the conventional open-loop system with 92.5% yield production. The results reveal that the optimal glucose feeding rate using GA is achieved satisfyingly and successfully

Fuzzy logic control of exothermic batch process

Thermal runaway of exothermic reaction, especially with batch chemical plants, is a critical issue because it can affect production quantity and quality, causes profit loss, and in worst case can threaten the safety of workers and lead to plant accident. The conventional PID controller which is usually used in many industries needs to be operated under supervision in order to perform the temperature control well. In this work, Mamdani type fuzzy logic controller is proposed to track an optimal reference temperature profile. Both process model and proposed controller are simulated using MATLAB. The results show the designed fuzzy logic controller is well-performed in set point tracking and able to cope with the simulated disturbance condition

Performance enhancement of a baffle-type solar heat collector through CDF simulation study

The application of solar energy conversion has been extensively utilized as an alternative energy source to generate heat. This approach would be a step towards sustainable energy development particularly in the manufacturing industry with energy-intensive process. In this paper, thermal enhancement on the key component of a solar energy device – solar heat collector (SHC), has been evaluated by proposing a baffle-type SHC with various geometric configuration in the air passage namely longitudinal baffle and transversal baffle. The performance of SHC is evaluated in term of efficiency, temperature distribution, airflow pattern and pressure drop across the collector outlet through Computational Fluid Dynamic (CFD) investigation. It was observed that maximum collector efficiency was achieved in the Longitudinal-SHC (L-SHC), with a value of 46.2 % followed by Transversal-SHC (T-SHC) and without baffles. Maximum drying temperature at the collector outlet was 332.43 K for L-SHC, showing temperature rise of 0.35 % and 4.21 % from T-SHC and without baffles, respectively. The velocity vector indicated that turbulence flow was created in the T-SHC which consequently improved the heat transfer. Whereas in L-SHC, enhancement was achieved through the prolonged heating time in the passage. Considering the thermo-hydraulic performance factor evaluated, these enhancement features had diminished the effect of pressure drop

Q-learning-based controller for fed-batch yeast fermentation

Industrial fed-batch yeast fermentation process is a typical nonlinear dynamic process that requires good control technique and monitoring to optimize the yeast production. This chapter explores the applicability of Q-learning in determining the feed flow rate in a fed-batch yeast fermentation process to achieve multiobjectives optimization. However, to develop such control system, the complex nature of the yeast metabolism that will affect the system stability has to be considered. Q-learning is well known for its interactive properties with the process environment and is suitable for the learning of system dynamic. Therefore, the utilization and performance of Q-learning to seek for the optimal gain for the controller is studied in this chapter. Meanwhile, the performance of Q-learning under the process disturbance is also tested. © Springer Science+Business Media New York 2013

Genetic-algorithm-based optimisation for exothermic batch process

The aim of this chapter is to optimise the productivity of an exothermic batch process, by maximising the production of the desired product while minimising the undesired by-product. During the process, heat is liberated when the reactants are mixed together. The exothermic behaviour causes the reaction to become unstable and consequently poses safety issues. In the industries, a dual-mode controller is used to control the process temperature according to a predetermined optimal reference temperature profile. However, the predetermined optimal reference profile is not able to limit the production of the undesired by-product. Hence, this work proposed a genetic-algorithm-based controller to optimise the batch productivity without referring to any optimal reference profile. From the simulation results, the proposed algorithm is able to improve the production of the desired product and reduce the production of the undesired by-product by 15.3 and 34.4 %, respectively. As a conclusion, the genetic-algorithm-based optimisation performs better in raw materials utilisation as compared to the predetermined optimal temperature profile method

Control of exothermic batch process using multivariable genetic algorithm

The aim of this research is to control the reactor temperature of an exothermic batch process. During the process, large amount of heat will be released rapidly when the reactants are mixed together. The exothermic behaviour causes the reaction to become unstable and consequently poses safety concern to the plant personnel. In practice, heat is needed to speed up the reaction rate so that the overall process cycle time can be reduced whereas the cooling is employed to cool down the reactor in order to reduce excessive heat. Hence, this paper proposes genetic algorithm (GA) to control the process temperature with a predetermined temperature profile. GA exploits the probabilistic search method to optimise the specific objective function based on the evolutionary principle. Simulation assessment of the GA has been carried out using a benchmark exothermic batch process model. The results show that GA is able to control the reactor temperature effectively

Multiancestry Genome-Wide Association Study of Lipid Levels Incorporating Gene-Alcohol Interactions

A person's lipid profile is influenced by genetic variants and alcohol consumption, but the contribution of interactions between these exposures has not been studied. We therefore incorporated gene-alcohol interactions into a multiancestry genome-wide association study of levels of high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides. We included 45 studies in stage 1 (genome-wide discovery) and 66 studies in stage 2 (focused follow-up), for a total of 394,584 individuals from 5 ancestry groups. Analyses covered the period July 2014-November 2017. Genetic main effects and interaction effects were jointly assessed by means of a 2-degrees-of-freedom (df) test, and a 1-df test was used to assess the interaction effects alone. Variants at 495 loci were at least suggestively associated (P <1 x 10(-6)) with lipid levels in stage 1 and were evaluated in stage 2, followed by combined analyses of stage 1 and stage 2. In the combined analysis of stages 1 and 2, a total of 147 independent loci were associated with lipid levels at P <5 x 10(-8) using 2-df tests, of which 18 were novel. No genome-wide-significant associations were found testing the interaction effect alone. The novel loci included several genes (proprotein convertase subtilisin/kexin type 5 (PCSK5), vascular endothelial growth factor B (VEGFB), and apolipoprotein B mRNA editing enzyme, catalytic polypeptide 1 (APOBEC1) complementation factor (A1CF)) that have a putative role in lipid metabolism on the basis of existing evidence from cellular and experimental models.Peer reviewe