Nilay Shah

An overview of CO2 capture technologies

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.

A general algorithm for short-term scheduling of batch operations—II. Computational issues

Abstract The first part of this paper (p. 211) presented a general mathematical framework for describing a wide variety of scheduling problems arising in multiproduct/multipurpose batch chemical plants. The problem is formulated as a large mixed integer linear programming model (MILP). We describe a variety of techniques that exploit the characteristics of the problem in order to reduce the amount of computation required. These include reformulation of some of the constraints, derivation of an alternative and much more compact linear programming relaxation of the MILP, and reduction of the non-integrality of the solutions of relaxed LPs through their a posteriori analysis. The combination of the three measures results in a significant improvement in computational performance without compromising the optimality of the solution obtained. A case study is presented to illustrate the applicability of the method to the scheduling of multipurpose plants under a variety of operational constraints.

Pharmaceutical supply chains: key issues and strategies for optimisation

Abstract Supply chain optimisation is now a major research theme in process operations and management. A great deal of research has been undertaken on facility location and design, inventory and distribution planning, capacity and production planning and detailed scheduling. Only a small proportion of this work directly addresses the issues faced in the pharmaceutical sector. On the other hand, this sector is very much ready for and in need of sophisticated supply chain optimisation techniques. At the supply chain design stage, a particular problem faced by this industry is the need to balance future capacity with anticipated demands in the face of the very significant uncertainty that arises out of clinical trials and competitor activity. Efficient capacity utilisation plans and robust infrastructure investment decisions will be important as regulatory pressures increase and margins are eroded. The ability to locate nodes of the supply chain in tax havens and optimise trading and transfer price structures results in interesting degrees of freedom in the supply chain design problem. Prior even to capacity planning comes the problem of pipeline and testing planning, where the selection of products for development and the scheduling of the development tasks requires a careful management of risk and potential rewards. At the operation stage, it is often difficult to ensure responsiveness. Most pharmaceutical products involve primary active ingredient (AI) production (often multi-stage chemical synthesis or bioprocess) and secondary (formulation) production. Both of the stages are characterised by low manufacturing velocities and are hampered by the need for quality assurance activities at several points. It is not unusual for the overall supply chain cycle time to be 300 days. In this environment, supply chain debottlenecking and decoupling strategies together with co-ordinated inventory management are crucial for quick responses to changing market trends. A good understanding of what actually drives the supply chain dynamics is also required. As often as not, erratic dynamics are introduced by business processes rather than by external demand, and may be eliminated by the re-design of internal business processes or supplier/customer relationships. This paper will consider important issues in supply chain design and operation drawn from the literature and from our collaborative research projects in this area. The main features of the problems will be reviewed as will the literature to date. Some strategies for solution will be identified, as will some future research needs.

Process industry supply chains: Advances and challenges

A large body of work exists in process industry supply chain optimisation. We describe the state of the art of research in infrastructure design, modelling and analysis and planning and scheduling, together with some industrial examples. We draw some conclusions about the degree to which different classes of problem have been solved, and discuss challenges for the future.

Mathematical programming techniques for crude oil scheduling

Abstract We consider the application of formal, mathematical programming techniques to the problem of scheduling the crude oil supply to a refinery. The relevant key decisions include the the allocation of crude oils to refinery and portside tanks, the connection of refinery tanks to crude distillation units (CDUs), the sequence and amounts of crudes pumped from the ports to the refineries, and the details relating to discharging of tankers at the portside. These decisions are typically made over a horizon of one month. Scheduling is important for two reasons: on the one hand, the economic penalties of poor scheduling are severe, and on the other, efficient scheduling techniques will enable the exploitation of opportunities e.g. unexpected cheap cargoes on the high seas. Typical approaches to this problem are based on user-driven simulations. This paper indicates how mathematical programming techniques can be applied to such problems, and highlights the advantages of using such approaches.

Monte Carlo evaluation of derivative-based global sensitivity measures

Abstract A novel approach for evaluation of derivative-based global sensitivity measures (DGSM) is presented. It is compared with the Morris and the Sobol’ sensitivity indices methods. It is shown that there is a link between DGSM and Sobol’ sensitivity indices. DGSM are very easy to implement and evaluate numerically. The computational time required for numerical evaluation of DGSM is many orders of magnitude lower than that for estimation of the Sobol’ sensitivity indices. It is also lower than that for the Morris method. Efficiencies of Monte Carlo (MC) and quasi-Monte Carlo (QMC) sampling methods for calculation of DGSM are compared. It is shown that the superiority of QMC over MC depends on the problems effective dimension, which can also be estimated using DGSM.

A combined optimization and agent-based approach to supply chain modelling and performance assessment

The main objective of this paper is to give an example of how expert systems techniques for distributed decision-making can be combined with contemporary numerical optimization techniques for the purposes of supply chain optimization and to describe the resulting software implementation. In this paper, multi-agent modelling techniques are applied to simulate and control a simple demand-driven supply chain network system, with the manufacturing component being optimized through mathematical programming. The system measures supply chain performance and the effect of different parameters in the replenishment control system, and can be used to simulate the behaviour of a system that uses optimization for part of its decision-making. The objective of this supply chain network system is to reduce operating cost, while maintaining a high level of customer order fulfilment.

A spatially explicit whole-system model of the lignocellulosic bioethanol supply chain: an assessment of decentralised processing potential

BackgroundLignocellulosic bioethanol technologies exhibit significant capacity for performance improvement across the supply chain through the development of high-yielding energy crops, integrated pretreatment, hydrolysis and fermentation technologies and the application of dedicated ethanol pipelines. The impact of such developments on cost-optimal plant location, scale and process composition within multiple plant infrastructures is poorly understood. A combined production and logistics model has been developed to investigate cost-optimal system configurations for a range of technological, system scale, biomass supply and ethanol demand distribution scenarios specific to European agricultural land and population densities.ResultsEthanol production costs for current technologies decrease significantly from

Effects of optimised plug-in hybrid vehicle charging strategies on electric distribution network losses

0.71 to

Logistical network design with robustness and complexity considerations

0.58 per litre with increasing economies of scale, up to a maximum single-plant capacity of 550 × 106 l year-1. The development of high-yielding energy crops and consolidated bio-processing realises significant cost reductions, with production costs ranging from