Controlling trace impurities in a dividing wall distillation column

Dividing wall distillation columns (DWCs) separate a feed mixture into three pure product streams using one column shell. Though attractive due to capital and operational savings, DWCs have yet to gain widespread industrial acceptance. One notable concern is controllability. The research within this document examines a four component feed mixture to evaluate the operational flexibility of a fixed-design DWC through experimental and simulation-based studies. A pilot DWC was successfully controlled at multiple operating points, and a dynamic model was developed to reflect the pilot dividing wall column. As a form of process intensification, DWCs have a higher risk for controller interaction making conventional PID control potentially inadequate. This work successfully used two PID temperature controllers to maintain the column at steady state, transition the column between steady states, and reject feed disturbances without controller interaction. These controller pairings were determined using conventional controller design techniques. Therefore, for this chemical system and column design, traditional approaches to distillation control are sufficient to handle the intensified nature of DWCs. Because more components are present in DWCs in larger amounts, there is concern that temperature control will no longer imply composition control. Temperature control proved successful in this study. Controlling two temperatures maintained column operation against feed disturbances. In addition, prefractionator temperature correlated well with reboiler duty for multiple feed qualities therefore serving as a promising control variable though more disturbances such as feed composition should be examined. The minimum energy controller was not tested experimentally. A steady state model with heat transfer matching the pilot data was scaled to the size of an industrial tower and used to generate a minimum energy response surface for different vapor and liquid split values. In summary, this research investigated the operational flexibility of a fixed-design DWC using a four component mixture, tested the ability of conventional distillation control design techniques to determine control structures for a DWC, and created a minimum energy operating surface that could be used to examine control structures. A technique to determine the overall heat transfer coefficients was developed, and the model closely matched experimental steady state data.Chemical Engineerin

Technology for large space systems: A bibliography with indexes (supplement 10)

The bibliography lists 408 reports, articles and other documents introduced into the NASA scientific and technical information system to provide helpful information to the researcher, manager, and designer in technology development and mission design in the area of large space system technology. Subject matter is grouped according to systems, interactive analysis and design, structural and thermal analysis and design, structural concepts and control systems, electronics, advanced materials, assembly concepts, propulsion, and solar power satellite systems

Aeronautical Engineering: A continuing bibliography, 1982 cumulative index

This bibliography is a cumulative index to the abstracts contained in NASA SP-7037 (145) through NASA SP-7037 (156) of Aeronautical Engineering: A Continuing Bibliography. NASA SP-7037 and its supplements have been compiled through the cooperative efforts of the American Institute of Aeronautics and Astronautics (AIAA) and the National Aeronautics and Space Administration (NASA). This cumulative index includes subject, personal author, corporate source, contract, and report number indexes

Knowledge discovery and data mining to understand and optimise the environmental behavior of wastewater treatment processes

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonDirect nitrous oxide (N2O) emissions during the biological nitrogen removal (BNR) processes can significantly increase the carbon footprint of wastewater treatment plant (WWTP) operations. However, quantifying the emissions and understanding the long-term behaviour of N2O fluxes in WWTPs remains challenging and costly. The aim of the current research is to combine wastewater domain knowledge with data-mining techniques to explain the long-term N2O emissions’ behaviour in full-scale biological reactors. A review of the recent full-scale N2O monitoring campaigns is conducted resulting in the development of an emission factor (EF) database with information on configurations, control strategies and operational conditions. The analysis focused on mechanistic model development, molecular biology methods and on the current data management and analysis practices (i.e. visualization techniques, statistical analysis). Sensor and laboratory data acquired from the N2O monitoring campaigns of mainstream and sidestream wastewater processes were used to develop, test and validate a methodological framework for knowledge discovery in wastewater databases. Abnormal events detection, structural changepoint detection, clustering, classification and regression algorithms are used in order to i) translate data into actionable information, ii) link N2O emissions ranges with specific operational conditions, iii) identify and isolate re-occurring system disturbances that affect performance, iv) predict the range of N2O emissions based on operational and environmental conditions and v) provide feedback to monitoring campaigns for the minimisation of sampling requirements. The analysis showed that the relationship of N2O emissions with the operational variables fluctuates in long-term monitoring campaigns; this should be taken into consideration for the development of mitigation measures and during the investigation of triggering operational conditions. Additionally, findings indicate that structural changepoints of operational variables monitored online can be used to detect changes in the behaviour and range of N2O emissions. Finally, data-driven models can reliably estimate N2O behaviour in wastewater processes under given operational conditions. However, fluctuation of dependencies, system disturbances and process-specific characteristics should be taken into consideration

Assessing plant design with regards to MPC performance using a novel multi-model prediction method

Model Predictive Control (MPC) is nowadays ubiquitous in the chemical industry and offers significant advantages over standard feedback controllers. Notwithstanding, projects of new plants are still being carried out without assessing how key design decisions, e.g., selection of production route, plant layout and equipment, will affect future MPC performance. The problem addressed in this Thesis is comparing the economic benefits available for different flowsheets through the use of MPC, and thus determining if certain design choices favour or hinder expected profitability. The Economic MPC Optimisation (EMOP) index is presented to measure how disturbances and restrictions affect the MPC’s ability to deliver better control and optimisation. To the author’s knowledge, the EMOP index is the first integrated design and control methodology to address the problem of zone constrained MPC with economic optimisation capabilities (today's standard in the chemical industry). This approach assumes the availability of a set of linear state-space models valid within the desired control zone, which is defined by the upper and lower bounds of each controlled and manipulated variable. Process economics provides the basis for the analysis. The index needs to be minimised in order to find the most profitable steady state within the zone constraints towards which the MPC is expected to direct the process. An analysis of the effects of disturbances on the index illustrates how they may reduce profitability by restricting the ability of an MPC to reach dynamic equilibrium near process constraints, which in turn increases product quality giveaway and costs. Hence the index monetises the required control effort. Since linear models were used to predict the dynamic behaviour of chemical processes, which often exhibit significant nonlinearity, this Thesis also includes a new multi-model prediction method. This new method, called Simultaneous Multi-Linear Prediction (SMLP), presents a more accurate output prediction than the use of single linear models, keeping at the same time much of their numerical advantages and their relative ease of obtainment. Comparing the SMLP to existing multi-model approaches, the main novelty is that it is built by defining and updating multiple states simultaneously, thus eliminating the need for partitioning the state-input space into regions and associating with each region a different state update equation. Each state’s contribution to the overall output is obtained according to the relative distance between their identification point, i.e., the set of operating conditions at which an approximation of the nonlinear model is obtained, and the current operating point, in addition to a set of parameters obtained through regression analysis. Additionally, the SMLP is built upon data obtained from step response models that can be obtained by commercial, black-box dynamic simulators. These state-of-the-art simulators are the industry’s standard for designing large-scale plants, the focus of this Thesis. Building an SMLP system yields an approximation of the nonlinear model, whose full set of equations is not of the user’s knowledge. The resulting system can be used for predictive control schemes or integrated process design and control. Applying the SMLP to optimisation problems with linear restrictions results in convex problems that are easy to solve. The issue of model uncertainty was also addressed for the EMOP index and SMLP systems. Due to the impact of uncertainty, the index may be defined as a numeric interval instead of a single number, within which the true value lies. A case of study consisting of four alternative designs for a realistically sized crude oil atmospheric distillation plant is provided in order to demonstrate the joint use and applicability of both the EMOP index and the SMLP. In addition, a comparison between the EMOP index and a competing methodology is presented that is based on a case study consisting of the activated sludge process of a wastewater treatment plant

Cattle vector-borne disease occurrence and management and climate change experiences in pastoral communities in Northern Tanzania

The livestock sector is vital to Tanzanian economy, and pastoralists largely depend on livestock production for their livelihood. The Vector-Borne Diseases (VBDs) of cattle, East Coast fever (ECF) and African animal trypanosomosis (AAT), whose occurrence are known to be influenced by climatic conditions, cause substantial cattle production losses in pastoralist communities that may be heightened with climate change. However, little is documented on pastoralists’ experiences and observations on climate change and ECF and AAT occurrence. Further, information on management practices for ECF and AAT is outdated following privatisation of veterinary services in Tanzania. This research employed 10 randomly selected villages of Monduli District in Northern Tanzania in 2014-2015. The study explored pastoralist indigenous knowledge of the relationship between climate parameters (temperature and rainfall) and ECF and AAT using participatory epidemiology approaches. The study also quantified the seasonal prevalence of Theileria parva and trypanosome infection in 960 cattle during the wet and dry seasons. Entomological surveys for brown ear ticks (Rhipicephalus appendiculatus) and tsetse flies (Glossina spp.) were also carried out. Assessment of management practices for ECF and AAT, seasonal movements and wildlife interactions in Maasai ecosystem were investigated. This research was the first to explore pastoralists’ understanding, observation and experiences on climate parameters and ECF and AAT. The findings on seasonal prevalence of T. parva and trypanosome infection will help inform decision-making on current and future cattle VBD control strategies. In addition, the information gathered from this thesis will inform the design and implementation of active surveillance, better control and preventive strategies to manage vectors and cattle vector-borne diseases in a changing climate in pastoral communities