Numerical Formulations For Attainable Region Analysis

Student Number : 9611112G - PhD thesis - School of Chemical and Metallurgical Engineering - Faculty of Engineering and the Built EnvironmentAttainable Region analysis is a chemical process synthesis technique that enables a design engineer to find process unit configurations that can be used to identify all possible outputs, by considering only the given feed specifications and permitted fundamental processes. The mathematical complexity of the attainable regions theory has so far been a major drawback in the implementation of this powerful technique into standard process design tools. In the past five years researchers focused on developing systematic methods to automate the procedure of identifying the set of all possible outputs termed the Attainable Regions. This work contributes to the development of systematic numerical formulations for attainable region analysis. By considering combinations of fundamental processes of chemical reaction, bulk mixing and heat transfer, two numerical formulations are proposed as systematic techniques for automation of identifying optimal process units networks using the attainable region analysis. The first formulation named the recursive convex control policy (RCC) algorithm uses the necessary requirement for convexity to approximate optimal combinations of fundamental processes that outline the shape of the boundary of the attainable regions. The recursive convex control policy forms the major content of this work and several case studies including those of industrial significance are used to demonstrate the efficiency of this technique. The ease of application and fast computational run-time are shown by assembling the RCC into a user interfaced computer application contained in a compact disk accompanying this thesis. The RCC algorithm enables identifying solutions for higher dimensional and complex industrial case studies that were previously perceived impractical to solve. The second numerical formulation uses singular optimal control techniques to identify optimal combinations of fundamental processes. This formulation also serves as a guarantee that the attainable region analysis conforms to Pontryagin’s maximum principle. This was shown by the solutions obtained using the RCC algorithm being consistent with those obtained by singular optimal control techniques

Optimal reactor network for methanol synthesis using RCC algorithm for attainable regions analysis

Optimal reactor network for methanol synthesis over Cu-Zn-Al catalyst has been developed by automated attainable regions analysis using the recursive convex control policy algorithm. Fundamental processes of solid catalysed gaseous reaction, cooling, mixing and heating are considered in order to develop a reactor network that can be used to attain specific optimal conditions such as maximum conversion or minimisation of the required heating or cooling surface area

Design model selection and dimensioning of anaerobic digester for the OFMSW

Abstract: In this study, we investigated the design model selection and dimensioning of the anaerobic digester for the codigestion of different organics fraction of municipal solid waste (OFMSW) originating from the city’s landfills. The waste quantification and characterization exercise were undertaken at the point of generation, so as to obtain the total amount of waste generated and to ascertain the waste composition. Via the application of the simple multi-attribute rating (SMART) technique of multiple-criteria decision analysis (MCDA) as a decision support tool base on cost, scalability, temperature regulation, ease of construction, operation, and maintenance. The most preferred model option for bioenergy design technology was selected from a list of potential alternatives available in the market. Continuous stirred tank reactor (digester) CSTR scored the highest with 79% and was selected for the design in OFMSW biogas production. The geometry of the biodigester parameters was comparable with the anaerobic digestion (AD) process

The kinetic of biogas rate from cow dung and grass clippings

Abstract:In this study, we investigated the use of laboratory batch anaerobic digester to derive kinetics parameters for anaerobic co-digestion of cow dung and grass clippings. The Carbon/Nitrogen (C/N) ratio of cow dung was found to be 17.17 and grass clippings to be 20.54. Through co-digestion, the C/N ratio settled at 19.08. Laboratory experimental data from 10 litres batch anaerobic digester operating at mesophilic temperature of 37 0C and pH of 6.9 was used to derive parameters for Modified Gompertz model. The actual biogas yield was found to be 4370 ml/g COD. In the model of biogas production prediction, the kinetics constants of A (ml/g COD), μ (ml/g COD. day), λ (day) were 4319.20, 939.71, 1.91 respectively with coefficient of determination (R2) of 0.996

Modelling the kinetic of biogas production from co-digestion of pig waste and grass clippings

Abstract: This work investigated the use of laboratory batch anaerobic digester to derive kinetics parameters for anaerobic co-digestion of pig waste and grass clippings. Laboratory experiment data from 10 litres batch anaerobic digester operating at ambient mesophilic temperature of 37 0C and pH of 6.9 was used to derive parameters for modified Gompertz model. The carbon/nitrogen (C/N) ratio of Pig waste was found to be 16.16 and grass clippings to be 20.54. Through co-digestion in ratio of 1:1, the C/N ratio settled at 17.28. The actual biogas yield was found to be 7725 ml/g COD. In the model of biogas production prediction, the kinetics constants of A (ml/g COD), μ (ml/g COD. day), λ (day) was 7920.70, 701.35, 1.61 respectively with coefficient of determination (R2) of 0.9994. Modified Gompertz plot showed better correlation of cumulative biogas production and these results show biogas production can be enhanced from co-digestion of substrates

Evaluation of Density-Based Models for the Solubility of Sclerocarya Birrea Kernel Oil in Supercritical Carbon Dioxide and the Formulation of a New Model

Solubility data obtained from literature for Sclerocarya birrea kernel oil in supercritical carbon dioxide (CO2) were correlated using six semi-empirical density-based models viz. Chastril, del Valle and Aguilera (DVA), Adachi and Lu (AL), Sparks et al., Kumar and Johnston (KJ), and Mendez-Santiago and Teja (MST). The determination coefficient values (R2) ranged from 0.72 to 0.95. The average absolute relative deviations (AARD%) ranged from 15.53 to 0.049. A comparison was made between all six semi-empirical density-based models, and it was concluded that the MST model provided an improved and better fit than the other models investigated. After examining each of the six models under investigation, an improved model is proposed, which can characterize most of the findings taken into account about Sclerocarya birrea kernel oil yield

Mesophilic anaerobic co-digestion of cow dung, chicken droppings and grass clippings

Abstract: The main focus of this study was mesophilic anaerobic co-digestion of cow dung, chicken droppings and grass clippings using pilot bio-digesters. The biochemical methane potential (BMP) works under batch anaerobic digester operating in ambient mesophilic temperature of 35 oC and 37 0C and pH of 7 to generate biogas. The carbon/nitrogen (C/N) ratio for cow dung and chicken droppings was found to be 17.70 and 63.67 respectively and grass clippings to be 20.54. Through co-digestion in a ratio of 1:1, the C/N ratio for cow dung and grass clippings settled at 19.19 while that for chicken droppings and grass clippings settled at 20.49. The conversion rate of the reaction and biogas production increased with the increase in temperature and hydraulic retention time until an equilibrium state was achieved. At the temperature 37 OC, it was observed to be the suitable mesophilic temperature for anaerobic digestion due to high dissociation and collision leading to high rate of biogas production

Multi-criteria analysis of different technologies for the bioenergy recovery from OFMSW

Abstract: In this study, the multi-criteria analysis model is demonstrated for evaluation and technologies from municipal solid waste (MSW) in City of Johannesburg (CoJ), South Africa. The technologies evaluation and alternation criteria for multi-criteria decision analysis (MCDA) area characterized by reviewing the literature and consulting experts in the renewable energy and waste management. MCDA was the approach employed by decision makers to make recommendation on technique employed to select the most suitable biogas digester technology for organic fraction of municipal solid waste (OFMWS) originating from the city’s landfills base on scalability, relative cost prices, available, temperature regulation, agitation, ease of construction, operation and maintenance. The result for digester type indicated that the “complete mix, continuously stirred anaerobic digester” (CSAD) was preferred with 79% preference to other anaerobic digester technologies for energy recovery

Formulations, development and characterization techniques of investment casting patterns

Conventionally, unfilled wax has been used as a universal pattern material for the investment casting process. With increase in demand for accurate dimensions and complex shapes, various materials have been blended with wax to develop more suitable patterns for investment casting in order to overcome performance limitations exhibited by unfilled wax. The present article initially reviews various investigations on the development of investment casting patterns by exploring pattern materials, type of waxes and their limitations, the effect of filler materials and various additives on unfilled wax, wax blends for pattern materials, plastics and polymers for pattern materials and 3D-printed patterns. The superiority of filled and polymer patterns in terms of dimensional accuracy, pattern strength, surface and flow properties over unfilled wax is also discussed. The present use of 3D patterns following their versatility in the manufacturing sector to revolutionize the investment casting process is also emphasized. Various studies on wax characterization such as physical (surface and dimensions), thermal (thermogravimetric analysis and differential scanning calorimetry), mechanical (thermomechanical analysis, tensile stress testing, dynamic mechanical analysis) and rheological (viscosity and shearing properties) are also discussed.The Technology Innovation Authority, South Africa.https://www.degruyter.com/view/j/revce2020-04-01am2019Chemical Engineerin

Process Control IV (Supplementary Examination)

Exam paper for second semester supplementary examination 2014, B.Tech. Chemical Engineering Technolog