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The combination of ethyl (hydroxyimino)cyanoacetate (Oxyma) and diisopropylcarbodiimide (DIC) has demonstrated superior performance in amino acid activation for peptide synthesis. However, it was recently reported that Oxyma and DIC could react to generate undesired hydrogen cyanide (HCN) at 20 °C, raising safety concerns for the practical use of this activation strategy. To help minimize the risks, there is a need for a comprehensive investigation of the mechanism and kinetics of the generation of HCN. Here we show the results of the first systematic computational study of the underpinning mechanism, including comparisons with experimental data. Two pathways for the decomposition of the Oxyma/DIC adduct are derived to account for the generation of HCN and its accompanying cyclic product. These two mechanisms differ in the electrophilic carbon atom attacked by the nucleophilic sp2-nitrogen in the cyclization step and in the cyclic product generated. On the basis of computed “observed” activation energies, ΔGobs⧧, the mechanism that proceeds via the attack of the sp2-nitrogen at the oxime carbon is identified as the most kinetically favorable one, a conclusion that is supported by closer agreement between predicted and experimental 13C NMR data. These results can provide a theoretical basis to develop a design strategy for suppressing HCN generation when using Oxyma/DIC for amino acid activation.

The SAFT‐ɣ Mie group‐contribution equation of state is used to represent the fluid‐phase behaviour of aqueous solutions of a variety of linear, branched, and cyclic amines. New group interactions are developed in order to model the mixtures of interest, including the like and unlike interactions between alkyl primary, secondary, and tertiary amine groups (NH2, NH, N), cyclic secondary and tertiary amine groups (cNH, cN), and cyclohexylamine groups (cCHNH, cCHN) with water (H2O). The group‐interaction parameters are estimated from appropriate experimental thermodynamic data for pure amines and selected mixtures. By taking advantage of the group‐contribution nature of the method, one can describe the fluid‐phase behaviour of mixtures of molecules comprising those groups over broad ranges of temperature, pressure, and composition. A number of aqueous solutions of amines are studied including linear, branched aliphatic, and cyclic amines. Liquid‐liquid equilibria (LLE) bounded by lower critical solution temperatures (LCSTs) have been reported experimentally and are reproduced here with SAFT‐ɣ Mie approach. The main feature of the approach is the ability not only to represent accurately the experimental data employed in the parameter estimation, but also to predict the vapour‐liquid, liquid‐liquid, and vapor‐liquid‐liquid equilibria, and LCSTs with the same set of parameters. Pure compound and binary phase diagrams of diverse types of amines and their aqueous solutions are assessed in order to demonstrate the main features of the thermodynamic and fluid‐phase behaviour.

The prediction of the solubility of active pharmaceutical ingredients (APIs) is a significant challenge which is of importance in pharmaceutical applications and solvent selection. Here, we extend the table of group interactions (3 like interactions, 47 unlike interactions) of the SAFT- γ Mie group-contribution equation of state to model the phase behaviour and solubility of mefenamic acid, a nonsteroidal anti-inflammatory drug, in a range of solvents. In addition to mefenamic acid, we also consider its molecular synthons: benzoic acid and diphenylamine. New experimental solubility data are presented for the three molecules in a range of solvents, and three new SAFT- γ Mie functional groups are defined (aCCOOH, aCNHaC and CH 3 CO) and characterised, together with their interactions with solvent groups. Literature data for the vapour pressure, single-phase density, saturation density, vapourisation enthalpy, bubble temperature, dew temperature, and bubble pressure are used to characterise the new group interactions. Solubility data are used to characterise the new group-group interactions only if there are no other experimental data available. The transferability and predictive accuracy of the new models are assessed by comparing the theoretical predictions with the experimental solubility data. Our comparison includes alcohols, ketones, and esters as families of solvents and mixed-solvent solubility predictions.

Empirical correlations for mass transfer coefficient and friction factor are often used in process models for reverse osmosis (RO) membrane systems. These usually involve four dimensionless groups, namely Reynolds number (Re), Sherwood number (Sh), friction factor (f), and Schmidt number (Sc), with the associated coefficients and exponents being obtained by fitting to experimental data. However, the range of geometric and operating conditions covered by the experiments is often limited. In this study, new dimensionless correlations for concentration polarization (CP) modulus and friction factor are presented, which are obtained by dimensional analysis and using simulation data from computational fluid dynamics (CFD). Two-dimensional CFD simulations are performed on three configurations of spacer-filled channels with 76 combinations of operating and geometric conditions for each configuration, covering a broad range of conditions encountered in RO membrane systems. Results obtained with the new correlations are compared with those from existing correlations in the literature. There is good consistency in the predicted CP with mean discrepancies less than 6%, but larger discrepancies for pressure gradient are found among the various friction factor correlations. Furthermore, the new correlations are implemented in a process model with six spiral wound modules in series and the predicted recovery, pressure drop, and specific energy consumption are compared with a reference case obtained by ROSA (Reverse Osmosis System Analysis, The Dow Chemical Company). Differences in predicted recovery and pressure drop are up to 5% and 83%, respectively, highlighting the need for careful selection of correlations when using predictive models in process design. Compared to existing mass transfer correlations, a distinct advantage of our correlations for CP modulus is that they can be directly used to estimate the impact of permeate flux on CP at a membrane surface without having to resort to the film theory.

The importance of developing accurate modeling tools for the prediction of reaction kinetics is well recognized. In this work, a thorough investigation of the suitability of quantum mechanical (QM) calculations to predict the effect of temperature on the rate constant of the reaction between ethane and the hydroxyl radical is presented. Further, hybrid models that combine a limited number of QM calculations and experimental data are developed in order to increase their reliability. The activation energy barrier of the reaction is computed using various computational methods, such as B3LYP, M05-2X, M06-2X, MP2 and PMP2, CBS-QB3, and W1BD, with a selection of basis sets. A broad range of values is obtained, including negative barriers for all of the calculations with B3LYP. The rate constants are also obtained for each method, using conventional transition state theory, and are compared with available experimental values at 298 K. The best agreement is achieved with the M05-2X functional with cc-pV5Z basis set. Rate constants calculated at this level of theory are also found to be in good agreement with experimental values at different temperatures, resulting in a mean absolute error of the logarithm (MAEln) of the calculated values of 0.213 over a temperature range of 200–1250 K and 0.108 over a temperature range of 300–499 K. Tunnelling and vibrational anharmonicities are identified as important sources of discrepancies at low and high temperatures, respectively. Hybrid models are proposed and found to provide good correlated rate-constant values and to be competitive with conventional kinetic models, i.e., the Arrhenius and the three-parameter Arrhenius models. The combination of QM-calculated and experimental data sources proves particularly beneficial when fitting to scarce experimental data. The parameters of the model built on the hybrid strategy have a significantly reduced uncertainty (reflected in the much narrower 95% confidence intervals) compared with the conventional kinetic models while also capturing well the experimental reaction rates with a MAEln of the rate constant of 0.118. This provides a useful strategy for kinetic model development.

A new one-dimensional predictive model for spiral wound modules (SWMs) applied to reverse osmosis membrane systems is developed by incorporating a detailed description of the geometric features of SWMs and considering flow in two directions. The proposed model is found to capture existing experimental data well, with similar accuracy to the widely-used plate model in which the SWM is assumed to consist of multiple thin rectangular channels. However, physical parameters that should in principle be model-independent, such as membrane permeability, are found to differ significantly depending on which model is used, when the same data sets are used for parameter estimation. Conversely, when using the same physical parameter values in both models, the water recovery predicted by the plate-like model is 12–20% higher than that predicted by the spiral model. This discrepancy is due to differences in the description of geometric features, in particular the active membrane area and the variable channel heights through the module, which impact on predicted performance and energy consumption. A number of design variables – the number of membrane leaves, membrane dimensions, centre pipe radius and the height of feed and permeate channels – are varied and their effects on performance, energy consumption and calculated module size are analysed. The proposed spiral model provides valuable insights into the effects of complex geometry on the performance of the SWM as well as of the overall system, at a low computational cost.

The manufacture of improved adhesive products that meet specified target properties has attracted increasing interest over the last decades. In this work, a general systematic methodology for the design of optimal adhesive products with low environmental impact is presented. The proposed approach integrates computer-aided design tools and Generalised Disjunctive Programming (GDP), a logic-based framework, to formulate and solve the product design problem. Key design decisions in product design (i.e., how many components should be included in the final product, which active ingredients and solvent compounds should be used and in what proportions) are optimised simultaneously. This methodology is applied to the design of solvent-based acrylic adhesives, which are commonly used in construction. First, optimal product formulations are determined with the aim to minimize toxicity. This reveals that number of components in the product formulation does not correlate with performance and that high performance can be achieved by investigating different number of components as well as by optimising all ingredients simultaneously rather than sequentially. The relation between two competing objectives (product toxicity and concentration of the active ingredient) is then explored by obtaining a set of Pareto optimal solutions. This leads to significant trade-offs and large areas of discontinuity driven by discrete changes in the list of optimal ingredients in the product.

The identification of improved carbon dioxide (CO2) capture solvents remains a challenge due to the vast number of potentially-suitable molecules. We propose an optimization-based computer-aided molecular design (CAMD) method to identify and select, from hundreds of thousands of possibilities, a few solvents of optimum performance for CO2 chemisorption processes, as measured by a comprehensive set of criteria. The first stage of the approach involves a fast screening stage where solvent structures are evaluated based on the simultaneous consideration of important pure component properties reflecting thermodynamic, kinetic, and sustainability behaviour. The impact of model uncertainty is considered through a systematic method that employs multiple models for the prediction of performance indices. In the second stage, high-performance solvents are further selected and evaluated using a more detailed thermodynamic model, i.e. the group-contribution statistical associating fluid theory for square well potentials (SAFT-γ SW), to predict accurately the highly non-ideal chemical and phase equilibrium of the solvent–water–CO2 mixtures. The proposed CAMD method is applied to the design of novel molecular structures and to the screening of a data set of commercially available amines. New molecular structures and commercially-available compounds that have received little attention as CO2 capture solvents are successfully identified and assessed using the proposed approach. We recommend that these solvents should be given priority in experimental studies to identify new compounds.

The introduction of Quality by Design in the pharmaceutical industry stimulates practitioners to better understand the relationship of materials, processes and products. One way to achieve this is through the use of targeted experimentation. In this study, an optimization framework to design experiments that effectively leverage parameterized process models is presented to maximize the space covered in the output variables while also obtaining an orthogonal bracketing study in the process input factors. The framework considers both multi‐objective and bilevel optimization methods for relating the two maximization objectives. Results are presented for two case studies—a spray coating process and a continuously stirred reactor cascade—demonstrating the ability to generate and identify efficient designs with fit‐for‐purpose trade‐offs between bracketed orthogonality in the input factors and volume explored in the process output space. The proposed approach allows a more complete understanding of the process to emerge from a small set of experiments.

Feed spacers are used in spiral wound reverse osmosis (RO) membrane modules to keep the membrane sheets apart as well as to enhance mixing. They are beneficial to membrane performance but at the expense of additional pressure loss. In this study, four types of feed spacer configurations are investigated, with a total of 20 geometric variations based on commercially available spacers and selected filament angles. The impact of feed spacer design on membrane performance is investigated by means of three-dimensional (3D) computational fluid dynamics (CFD) simulations, where the solution-diffusion model is employed for water and solute transport through RO membranes. Numerical simulation results show that, for the operating and geometric conditions examined, fully woven spacers outperform other spacer configurations in mitigating concentration polarisation (CP). When designed with a mesh angle of 60°, fully woven spacers also deliver the highest water flux, although the associated pressure drops are slightly higher than their nonwoven counterparts. Middle layer geometries with a mesh angle of 30° produce the lowest water flux. On the other hand, spacers with a mesh angle of 90° show the lowest pressure drop among all the filament arrangements examined. Furthermore, the computational model presented here can also be used to predict membrane performance for a given feed spacer type and geometry.

The choice of solvent in which to carry out liquid-phase organic reactions often has a large impact on reaction rates and selectivity and is thus a key decision in process design. A systematic methodology for solvent design that does not require any experimental data on the effect of solvents on reaction kinetics is presented. It combines quantum mechanical computations for the reaction rate constant in various solvents with a computer-aided molecular design (CAMD) formulation. A surrogate model is used to derive an integrated design formulation that combines kinetics and other considerations such as phase equilibria, as predicted by group contribution methods. The derivation of the mixed-integer nonlinear formulation is presented step-by-step. In the application of the methodology to a classic S N 2 reaction, the Menschutkin reaction, the reaction rate is used as the key performance objective. The results highlight the trade-offs between different chemical and physical properties such as reaction rate constant, solvent density and solid reactant solubility and lead to the identification of several promising solvents to enhance reaction performance.

Hydrogen is regarded as a leading candidate for alternative future fuels. Solid oxide electrolyser cells (SOEC) may provide a cost-effective and green route to hydrogen production especially when coupled to a source of renewable electrical energy. Developing an understanding of the response of the SOEC stack to transient events that may occur during its operation with intermittent electricity input is essential before the realisation of this technology. In this paper, a one-dimensional (1D) dynamic model of a planar SOEC stack has been employed to study the dynamic behaviour of such an SOEC and the prospect for stack temperature control through variation of the air flow rate. Step changes in the average current density from 1.0 to 0.75, 0.5 and 0.2 A/cm have been imposed on the stacks, replicating the situation in which changes in the supply of input electrical energy are experienced, or the sudden switch-off of the stack. Such simulations have been performed both for open-loop and closed-loop cases. The stack temperature and cell voltage are decreased by step changes in the average current density. Without temperature control via variation of the air flow rate, a sudden fall of the temperature and the cell potential occurs during all the step changes in average current density. The temperature excursions between the initial and final steady states are observed to be reduced by the manipulation of the air flow rate. Provided that the change in the average current density does not result in a transition from exothermic to endothermic operation of the SOEC, the use of the air flow rate to maintain a constant steady-state temperature is found to be successful. © 2010 Higher Education Press and Springer-Verlag Berlin Heidelberg.

The Supergen fuel cell programme is the UK's flagship fuel cell research consortium, funded by the Research Councils. Established just under four years ago it brings together four academic partners (Imperial College, Newcastle University, Nottingham University and St Andrews University), three industry partners (Johnson Matthey, Rolls-Royce Fuel Cell systems and Ceres Power), and the UKs defence laboratory (DSTL), to work together to bring fundamental science to address key industry challenges in the fuel cell sector. This presentation will review progress made in tackling a number of key issues in solid oxide fuel cells, namely the constrained sintering of thick film electrolytes, electrolytes with higher conductivity at lower temperature, the relationship between fuel composition and anode behaviour - including work on sulphur poisoning and new anode materials, and new methods to characterize and model electrode performance.

A hybrid experimental/computer-aided methodology for the design of solvents for reactions is presented. It is based on the use of a few reaction rate measurements to build a reaction model, followed by the formulation and solution of an optimal computer-aided molecular design problem (CAMD) in which the reaction rate under given conditions is maximized. In order to verify the suitability of the solvent candidates identified in the CAMD step, and to assess the reliability of the model used, feedback can be introduced. When the reliability of the model is found to be insufficient, experimental rate data for the candidate solvents are obtained and added to the original data set to create an updated reaction model, which can be used to find new candidate solvents. Since very few measurements are used to build the reaction model, we perform a sensitivity analysis on the model to assess the impact of uncertainty. Using this information to generate scenarios, we then solve a stochastic optimization problem, which aims to determine the solvents that give the best average performance. The final output consists of a list of candidate solvents which can be targeted for experimentation. This methodology is illustrated, step by step, through application to a solvolysis reaction. © 2007 American Institute of Chemical Engineers AIChE J, 2007

Solid oxide fuel cell (SOFC) based micro combined heat and power (micro-CHP) systems exhibit fundamentally different characteristics from other common micro-CHP technologies. Of particular relevance to this article is that they have a low heat-to-power ratio and may benefit from avoidance of thermal cycling. Existing patterns of residential heat demand in the UK, often characterised by morning and evening heating periods, do not necessarily complement the characteristics of SOFC based micro-CHP in an economic and technical sense because of difficulties in responding to large rapid heat demands (low heat-to-power ratio) and preference for continuous operation (avoidance of thermal cycling). In order to investigate modes of heat delivery that complement SOFC based micro-CHP a number of different heat demand profiles for a typical UK residential dwelling are considered along with a detailed model of SOFC based micro-CHP technical characteristics. Economic and environmental outcomes are modelled for each heat demand profile. A thermal energy store is then added to the analysis and comment is made on changes in economic and environmental parameters, and on the constraints of this option. We find that SOFC-based micro-CHP is best suited to slow space heating demands, where the heating system is on constantly during virtually all of the winter period. Thermal energy storage is less useful where heat demands are slow, but is better suited to cases where decoupling of heat demand and heat supply can result in efficiencies.

A systems modelling framework has been developed for the simultaneous design and operations scheduling of a biomass to heat supply chain. A state-task-network (STN) approach, originally developed for the design and scheduling of batch plants within the process industries, has been adapted to provide a generic representation of harvesting, densification, drying, storage and transportation activities. This facilitates effective system scheduling under dynamic system influences affecting harvested yield, crop moisture content, ambient drying rates and seasonal demand. Period-specific harvest tasks, biomass moisture content variations throughout the dynamic processing chain and concurrent ambient drying in storage processes have been captured analytically and integrated into the modelling framework. The model is applied to a 12-month operational cycle for a hypothetical 20 MWth peak output biomass-to-heat supply chain system. Optimized supply chain design and operational schedules are analysed; land and operational harvesting costs are observed to dominate supply chain economics. The optimized design and operational schedules are also compared with those derived through a simple heuristic strategy in order to assess potential economic benefits of applying systems optimization methods. Results suggest that improvements of 5–25% on cost minimization objectives can be achieved; equivalent to £0.6–3.0 MW h th − 1 in delivered heat costs.

Solvent-based postcombustion carbon dioxide (CO2) capture requires minimum retrofitting of current CO2-emitting power plants but is challenging because of the high energy penalty in solvent regeneration and the environmental impacts of solvent degradation. Research efforts are predominantly based on lab and pilot-scale experiments to select solvents and process systems which improve the overall performance of this technology. Notwithstanding the value of the experimental efforts, this study proposes an efficient computational approach for screening a vast number of commercial and novel solvents and process configurations. Computer-aided molecular design, advanced group contribution methods, process synthesis, and multicriteria sustainability assessment are combined to provide new insights in solvent-based CO2 capture. This study provides details of the data requirements, highlights several high-performance solvents and process configurations, and quantifies the benefits from economic, life cycle, and hazard assessment perspective. Thus, it also provides information for the experimental approaches, focusing on a narrower, near-optimum design space.

Solid oxide fuel cell combined heat and power (CHP) is a promising technology to serve electricity and heat demands. In order to analyse the potential of the technology, a detailed techno-economic energy-cost minimisation model of a micro-CHP system is developed drawing on steady-state and dynamic SOFC stack models and power converter design. This model is applied it to identify minimum costs and optimum stack capacities under various current density change constraints. Firstly, a characterisation of the system electrical efficiency is developed through the combination of stack efficiency profiles and power converter efficiency profiles. Optimisation model constraints are then developed, including a limitation in the change of current density (A cm−2) per minute in the stack. The optimisation model is then presented and further expanded to account for the inability of a stack to respond instantaneously to load changes, resulting in a penalty function being applied to the objective function proportional to the size of load changes being serviced by the stack. Finally, the optimisation model is applied to examine the relative importance, in terms of minimum cost and optimum stack maximum electrical power output capacity, of the limitation on rate of current density change for a UK residential micro-CHP application. It is found that constraints on the rate of change in current density are not an important design parameter from an economic perspective.

In operation, solid oxide fuel cells ( SOFC s) can be subjected to frequent load changes due to variable power demand. Knowledge of their dynamic behaviour is thus important when looking for suitable control strategies. The present work investigates the open and closed-loop transient response of a co-flow planar anode-supported intermediate-temperature direct internal reforming solid oxide fuel cell to load step-changes. A previously developed dynamic SOFC model, which consists of mass and energy balances and an electrochemical model that relates the fuel and air gas compositions and temperature to voltage, current density, and other relevant fuel cell variables, is used. A master controller that imposes a current density disturbance representing a change in power demand and sets the fuel and air flow rates proportional to that current (keeping the fuel utilisation and air ratio constant) and a typical feedback PID temperature controller that, given the outlet fuel temperature, responds by changing the air ratio around the default set by the master controller, have been implemented. Two distinct control approaches are considered. In the first case, the controller responds to a fixed temperature set-point, while in the second one the set-point is an adjustable parameter that depends on the magnitude of the load change introduced. Open-loop dynamic simulations show that, after a positive/negative load step-change, the overall SOFC temperature increases/decreases and the intermediate period between the disturbance imposed and the new steady-state is characterised by an undershoot/overshoot of the cell potential. Closed-loop simulations when load step-changes from 0.5 to 0.3, 0.4, 0.6, and 0.7 A cm−2 are imposed show that the proposed fixed set-point PID controller can successfully take the outlet fuel temperature to the desired set-point. However, it is also shown that for load changes of higher magnitude, an adjustable set-point control strategy is more effective in avoiding oscillatory control action, which can often lead to operation failure, as well as in preventing potentially damaging temperature gradients that can cause excessive stresses within the SOFC components and lead to cell breakdown.

Increasing demand for power and the depletion of fossil fuels are providing opportunities for the development of fuel cells as power generating systems. This paper investigates the integration of a solid oxide fuel cell (SOFC) with biomass gasification for the production of power and heat (combined heat and power (CHP) system). A steady-state model was developed in the gPROMS modelling tool to investigate the integrated system. The system was modelled for two different options, a cold process involving gas cleaning at a reduced temperature and a hot process involving gas cleaning at a high temperature. For each option, the model was used to determine the system efficiency and prospective costs. The electrical efficiency and overall system efficiency for the hot process were found to be 23 and 60% and for the cold process the efficiencies were 21 and 34%, respectively. Superior heat management in the gas cleaning stage of the hot process results in its higher system efficiency. The capital cost for the hot process appears higher than that for the cold process. This differential capital cost may be justified by the income earned from selling the extra heat produced in the hot process. Conversely, the cold process produces a gas stream with lower levels of impurities than the hot process.

A mixed-integer dynamic optimization (MIDO) framework for solvent design in batch processes is presented. Performance measures reflecting process economics and computed on the basis of process dynamics are used to validate candidate solvent structures built from the UNIFAC molecular groups. These define the discrete space in the overall material formulation and are combined according to molecular design feasibility rules to guarantee realistic molecular representations. The algorithm is based on the decomposition of the MIDO primal subproblem into several steps that are solved successively. This allows unsuitable solvents to be detected and discarded quickly and without significant computational cost. Emphasis is placed on problem formulation in order to match accuracy of process model and physical property predictions. The algorithm is applied successfully to an industrial case study dealing with a three-phase dehydration column and a decantation unit for solvent recovery. The proposed algorithm can be regarded as a promising initial step toward an integrated and simultaneous methodology for material process design in batch separation systems.

The effectiveness of any methodology used to identify hazards in chemical processes affects both safety and economics. To achieve maximum safety at minimum cost, a conservative, but realistic, analysis must be carried out. An approach to hazard identification is proposed based on a detailed process model which includes nonlinear dynamics and uncertainty. A new modeling framework, the region-transition model (RTM), is developed, which enables the simulation of regions of the operating space through an extension of the hybrid state transition system formalism. The RTM is illustrated on a nonlinear batch reactor with parameter uncertainty. A safety-verification algorithm identifies regions of the input space (initial conditions and external inputs) which guarantee safe operation. The algorithm is successfully applied to three examples: a tank with overflow and underflow, a batch reactor with an exothermic reaction, and a CSTR with feed preheating.

A novel quantitative approach to identify the hazards associated with a process is presented. It is based on the analysis of a “region-transition model” of the process which, through the concepts of hybrid state transition systems and interval arithmetic, accounts for both nonlinearity and uncertainty. A safety verification algorithm which evolves the model over a given time horizon is able to determine which hazards can occur. As required in safety analysis, the methodology always provides a conservative appraisal of the dangers, based on the best model available. An exothermic reaction taking place in a batch reactor serves as an illustrative example.

Hydrogen production using solid oxide electrolyser cells (SOECs) has attracted increasing research attention as it may provide a cost-effective and green route to hydrogen generation especially when coupled to a source of renewable or nuclear energy. Developing control strategies for the SOEC stack to respond to changes or disturbances that may occur during its operation is necessary to support the development and demonstration of this technology. A one-dimensional (1D) dynamic model of a planar SOEC stack developed at Imperial College has been employed to study optimal control strategies. In this paper, some preliminary results are reported for two control strategies during a change of operating regime - maximizing hydrogen production and minimizing electrical energy consumption. The results offer optimal control policies for the chosen situations and provide a good starting point for identifying the optimal control strategy in practical operation. © 2012 Elsevier B.V.

Since oil and gas production is moving to deeper waters, subsen pipelines are being subjected to higher pressures and lower temperatures. Under such conditions, the formation of hydrates is promoted. Hydrates are solid, non-flowing compounds of gas and water whose formation can cause line blockages, with the consequent economical losses and safety risks. The increasing hydrate formation propensity suggests the necessity to predict the possibility of hydrate formation in on-line operation so as to take preventive control actions and thereby provide flow assurance. Although a detailed dynamic model will enable the prediction of the possibility of hydrate formation, model inaccuracies and process disturbances will make this prediction less accurate. The usage of key available measurements will enable to address these disadvantages. The aim of this paper is to develop a combined state and parameter estimator for this process, by combining a dynamic model with available measurements.

Flow assurance in deep-water developments has been identified as one of the main technological problems that the oil and gas industry faces today. Extreme conditions such as high pressures and low temperatures promote the formation of gas hydrates that can potentially reduce or completely block the flow path, causing severe financial losses. An integrated framework for model-based flow assurance management is presented. A two-phase flow model describing the thermal-hydraulic dynamics of subsea pipelines is coupled with a hydrate thermodynamic equilibrium calculation module. The model-based flow assurance framework determines whether hydrate formation can occur at every time instant and at every point along the pipe by comparing the hydrate formation equilibrium temperature and the actual pipeline temperature. The injection of hydrate thermodynamic inhibitors (e.g. methanol) is also included in the model. The framework is implemented in a state-of-the-art modelling tool (gPROMS®). In order to demonstrate its capabilities, shut-in and re-start transient production scenarios are evaluated. Our studies illustrate the benefits of a model-based approach in dealing with the complex and multi-faceted problem of flow assurance.

Technological innovation in process design often leads to increased technological risk arising from incomplete knowledge. We propose a systematic approach to manage this risk using mathematical models that are sufficiently detailed to quantify risk. Global sensitivity analysis is used to determine the complete probability distributions for the key performance indicators of the process, thereby allowing informed decisions to be taken regarding the acceptability of the risk inherent in a given design. It also produces global sensitivity indices which allow the identification of the critical uncertain parameters on which additional R&D needs to be foused if the risk is deemed to be unacceptably high. If the risk is acceptable, then scenario-based approximation is used to handle the residual uncertainty in the critical parameters. Issues regarding the robust and efficient solution of problems involving large numbers of scenarios based on nonlinear models with thousands of variables are considered. The methodology is demonstrated via a case study concerning the design of a catalytic tubular reactor.

This chapter describes the design of polyolefin reactor mixtures. The main uses of polyolefins today are in packaging, giving a substantial environmental benefit by reducing the weight of goods to be transported and thereby reducing the amount of hydrocarbons burnt by lorries. Without polyolefins it would also be difficult to imagine today's supermarket where food and goods are packed in clear plastics, giving the customer the opportunity to inspect goods prior to purchase. A number of production processes have been developed over the years making the production and sale of polyolefins a very competitive business. The key to financial success in the production of bulk poiyolefins lies in the continual improvement of design and operation, which can be achieved through a better understanding of the physical phenomena that underpin the production process. In particular, this work shows that a knowledge of the thermodynamic behavior of the olefin and polyolefin can result in significant improvements in productivity.

This paper deals with a novel, quantitative approach to process safety verification under uncertainty in either model parameters or process inputs and disturbances. The work is based on the concept of a region-transition model- (Adjiman, 1999) .

In this paper, three of the leading options for large scale CO2 capture are reviewed from a technical perspective. We consider solvent-based chemisorption techniques, carbonate looping technology, and the so-called oxyfuel process. For each technology option, we give an overview of the technology, listing advantages and disadvantages. Subsequently, a discussion of the level of technological maturity is presented, and we conclude by identifying current gaps in knowledge and suggest areas with significant scope for future work. We then discuss the suitability of using ionic liquids as novel, environmentally benign solvents with which to capture CO2. In addition, we consider alternatives to simply sequestering CO2-we present a discussion on the possibility of recycling captured CO2 and exploiting it as a C1 building block for the sustainable manufacture of polymers, fine chemicals, and liquid fuels. Finally, we present a discussion of relevant systems engineering methodologies in carbon capture system design.