Development of systematic technique for energy and property integration in batch processes

The increasing consumption of energy, generation of waste as well as higher cost of fresh resources and waste treatment systems are the important driving forces for developing efficient, environmentally friendly and economic resource conservation techniques in the process industries. Process integration is being recognized as an useful systematic strategy for resource conservation and waste minimization. Up to date, less research works have been investigated on heat and property integration and these works are only focused on continuous processes.Since the application of batch processes is increasingly popular due to the development of technology-intensive industries such as pharmacy, fine chemistry and foods, it is necessary to consider both heat and property integration in batch processes simultaneously. In this thesis, a new mixed integer nonlinear programming (MINLP) mathematical model is introduced to synthesize a property-based heat integrated resource conservation networks (HIRCNs) for batch processes. A source-HEN-sink superstructure is constructed to embed all possible network configurations. Then, an MINLP model that consists of propertybased resource conservation network (RCN) and heat exchanger network (HEN) models is developed.In the proposed model, the property-based RCN model is formulated based on supertargeting approach while HEN model is formulated via automated targeting method (ATM). The optimization objective is to minimize total annualized cost (TAC) for a batch process system. This includes the operating cost of fresh resources, hot and cold utilities as well as the capital cost of storage tanks. To demonstrate the proposed approach, three case studies were solved. Based on the optimized results, the proposed simultaneous targeting approach for property-based HIRCNs is more effective in term of TAC for HIRCNs than the presented sequential targeting approach

Dynamic model of isothermal moving bed reducer for chemical looping hydrogen production

This paper investigates process modelling and reactor design for the reducer in the chemical looping hydrogen production (CLHP) process. The CLHP process adopts a three-reactor technology that can provide an efficient and sustainable alternative to the current hydrogen production technology via steam methane reforming (SMR), which suffers from several limitations during industrial operation. CLHP can achieve higher thermal efficiency than SMR and provide a carbon capture and storage (CCS) system. So far, no report on the modelling analysis of the reducer despite its critical dependence on temperature. The modelling study adopts the modified pellet-grain model at the micro-scale and counter-current moving bed model reactor at the reactor level. Simulation results of the gas-solid behavior based on the multi-scale model agree with the literature evidence. Critical information from the model revealed that the oxygen carriers (solids) can attain a desired state, but the syngas remains underutilized. The model simulation further suggests that lowering the gas-solid velocity ratio (Vgs) can substantially promote the syngas conversion. However, the Vgs value must remain above a threshold value (170), defined through the limitation of gas-solid velocities in a moving bed reactor. Since a CCS system requires high purity (>95%) of the product gas, rigorous temperature-pellet size optimization is vital to achieving the target purity while maintaining desired solid state

Biohydrogen production: A new controllability criterion for analyzing the impacts of dark fermentation conditions

Biohydrogen production from renewable resources using dark fermentation has become an increasingly attractive solution in sustainable global energy supply. So far, there has been no report on the controllability analysis of biohydrogen production using dark fermentation. Process controllability is a crucial factor determining process feasibility. This paper presents a new criterion for assessing biohydrogen process controllability based on PI control. It proposes the critical loop gain derived via Routh stability analysis as a measure of process controllability. Results show that the dark fermentation using the bacteria from anaerobic dairy sludge and substrate source from sugarcane vinasse can lead to a highly controllable process with a critical loop gain value of 4.3. For the two other cases, an increase of substrate concentration from 10 g/L to 40 g/L substantially reduces the controllability. The proposed controllability criterion is easily adopted to assess the process feasibilty based on experimental data

A comparison study between different kernel functions in the least square support vector regression model for penicillin fermentation process

Soft sensors are becoming increasingly important in our world today as tools for inferring difficult-to-measure process variables to achieve good operational performance and economic benefits. Recent advancement in machine learning provides an opportunity to integrate machine learning models for soft sensing applications, such as Least Square Support Vector Regression (LSSVR) which copes well with nonlinear process data. However, the LSSVR model usually uses the radial basis function (RBF) kernel function for prediction, which has demonstrated its usefulness in numerous applications. Thus, this study extends the use of non-conventional kernel functions in the LSSVR model with a comparative study against widely used partial least square (PLS) and principal component regression (PCR) models, measured with root mean square error (RMSE), mean absolute error (MAE) and error of approximation (Ea) as the performance benchmark. Based on the empirical result from the case study of the penicillin fermentation process, the Ea of the multiquadric kernel (MQ) is lowered by 63.44% as compared to the RBF kernel for the prediction of penicillin concentration. Hence, the MQ kernel LSSVR has outperformed the RBF kernel LSSVR. The study serves as empirical evidence of LSSVR performance as a machine learning model in soft sensing applications and as reference material for further development of non-conventional kernels in LSSVR-based models because many other functions can be used as well in the hope to increase the prediction accuracy

The application of machine learning in nanoparticle treated water: A review

Pollution from industrial effluents and domestic waste are two of the most common sources of environmental pollutants. Due to the rising population and manufacturing industries, large amounts of pollutants were produced daily. Therefore, enhancements in wastewater treatment to render treated wastewater and provide effective solutions are essential to return clean and safe water to be reused in the industrial, agricultural, and domestic sectors. Nanotechnology has been proven as an alternative approach to overcoming the existing water pollution issue. Nanoparticles exhibit high aspect ratios, large pore volumes, electrostatic properties, and high specific surfaces, which explains their efficiency in removing pollutants such as dyes, pesticides, heavy metals, oxygen-demanding wastes, and synthetic organic chemicals. Machine learning (ML) is a powerful tool to conduct the model and prediction of the adverse biological and environmental effects of nanoparticles in wastewater treatment. In this review, the application of ML in nanoparticle-treated water on different pollutants has been studied and it was discovered that the removal of the pollutants could be predicted through the mathematical approach which included ML. Further comparison of ML method can be carried out to assess the prediction performance of ML methods on pollutants removal. Moreover, future studies regarding the nanotoxicity, synthesis process, and reusability of nanoparticles are also necessary to take into consideration to safeguard the environment

Effect of Ethrel as a Flower Induction Agent on the Growth and Quality of Fresh Golden Pineapple (MD2) in Malaysia

Ethrel was proposed as a good flowering agent to induce the flowering of various fresh pineapples. However, very limited research studies have been carried out on the effect of this inducing agent on the growth of the golden pineapple or Millie Dillard (MD2) in Malaysia, with none in Sarawak. To address this research gap, this study aims to investigate the effect of ethrel on the growth and fruit quality of MD2 pineapples growth in Miri, Sarawak. In this study, ethrel acts as an induction agent that was applied to induce the pineapples at maturity around 11 months after planting (MAP). Moreover, these induced pineapples were harvested 15 MAPs, whereas no pineapples were available for harvesting from the control group that was induced by natural flowering. These results showed that ethrel provided a higher yield in the number of pineapples compared to natural flowering, classifying them as Grade B pineapples. For the growth and fruit quality of the MD2 pineapples, it was found that the average values for the total soluble solids (TSS), total titratable acidity (TTA), pH, diameter, height with a crown, and whole fruit fresh weights with the crown of the pineapples were 16.48 Brix, 0.54 %, pH 3.89, 11.7 cm, 40.3 cm, and 1.4 kg, respectively. Furthermore, the average TSS to TTA ratio was 32.52, which was within the range of 5.5 to 66.4, indicating that the pineapples were sweet with prospects for commercial selling. Hence, it can be concluded that using ethrel as an induction agent is significant in Malaysia

Adaptive Soft Sensors for Non-Gaussian Chemical Process Plant Data Based on Locally Weighted Partial Least Square

This thesis proposes an improved algorithm attributed to its abilities to deal with non-Gaussian distributed and nonlinear data and missing measurements. It was formulated through a modification on locally weighted partial least square by incorporating an ensemble method, Kernel function and independent component analysis and expectation maximisation algorithms. The algorithm was then tested using process data generated from six simulated plants. Simulation results indicate superiority of this algorithm compared to the existing algorithms

Biohydrogen production: A new controllability criterion for analyzing the impacts of dark fermentation conditions

Biohydrogen production from renewable resources using dark fermentation has become an increasingly attractive solution in sustainable global energy supply. So far, there has been no report on the controllability analysis of biohydrogen production using dark fermentation. Process controllability is a crucial factor determining process feasibility. This paper presents a new criterion for assessing biohydrogen process controllability based on PI control. It proposes the critical loop gain derived via Routh stability analysis as a measure of process controllability. Results show that the dark fermentation using the bacteria from anaerobic dairy sludge and substrate source from sugarcane vinasse can lead to a highly controllable process with a critical loop gain value of 4.3. For the two other cases, an increase of substrate concentration from 10 g/L to 40 g/L substantially reduces the controllability. The proposed controllability criterion is easily adopted to assess the process feasibilty based on experimental data

Dynamic model of isothermal moving bed reducer for chemical looping hydrogen production

This paper investigates process modelling and reactor design for the reducer in the chemical looping hydrogen production (CLHP) process. The CLHP process adopts a three-reactor technology that can provide an efficient and sustainable alternative to the current hydrogen production technology via steam methane reforming (SMR), which suffers from several limitations during industrial operation. CLHP can achieve higher thermal efficiency than SMR and provide a carbon capture and storage (CCS) system. So far, no report on the modelling analysis of the reducer despite its critical dependence on temperature. The modelling study adopts the modified pellet-grain model at the micro-scale and counter-current moving bed model reactor at the reactor level. Simulation results of the gas-solid behavior based on the multi-scale model agree with the literature evidence. Critical information from the model revealed that the oxygen carriers (solids) can attain a desired state, but the syngas remains underutilized. The model simulation further suggests that lowering the gas-solid velocity ratio (Vgs) can substantially promote the syngas conversion. However, the Vgs value must remain above a threshold value (170), defined through the limitation of gas-solid velocities in a moving bed reactor. Since a CCS system requires high purity (>95%) of the product gas, rigorous temperature-pellet size optimization is vital to achieving the target purity while maintaining desired solid state

The application of machine learning in nanoparticle treated water: A review

Pollution from industrial effluents and domestic waste are two of the most common sources of environmental pollutants. Due to the rising population and manufacturing industries, large amounts of pollutants were produced daily. Therefore, enhancements in wastewater treatment to render treated wastewater and provide effective solutions are essential to return clean and safe water to be reused in the industrial, agricultural, and domestic sectors. Nanotechnology has been proven as an alternative approach to overcoming the existing water pollution issue. Nanoparticles exhibit high aspect ratios, large pore volumes, electrostatic properties, and high specific surfaces, which explains their efficiency in removing pollutants such as dyes, pesticides, heavy metals, oxygen-demanding wastes, and synthetic organic chemicals. Machine learning (ML) is a powerful tool to conduct the model and prediction of the adverse biological and environmental effects of nanoparticles in wastewater treatment. In this review, the application of ML in nanoparticle-treated water on different pollutants has been studied and it was discovered that the removal of the pollutants could be predicted through the mathematical approach which included ML. Further comparison of ML method can be carried out to assess the prediction performance of ML methods on pollutants removal. Moreover, future studies regarding the nanotoxicity, synthesis process, and reusability of nanoparticles are also necessary to take into consideration to safeguard the environment