Volume 6, Issue 2 - CAPE Forum 2011

Process engineering is playing an increasingly vital role in meeting the current and future needs of both Society and the planet Earth, from carbon capture to sustainable energy generation, from water supply to waste management, and from food agrochemicals to pharmaceutical products. Meeting the exponentially growing energy and water demands that are required to improve the Quality of Life, and securing sustainable energy and water supplies, are important challenges for today’s process engineers.The School of Engineering, Design and Technology at the University of Bradford hosted the Computer Aided Process Engineering Forum (CAPE FORUM) 2011 on 21st and 22nd March 2011. The event was organised on behalf of the CAPE Working Party of the European Federation of Chemical Engineering. Its aim is to encourage young academic and industrial researchers to present their stimulating research and knowledge transfer ideas in energy, water, and other areas.Over the two days the event saw keynote speakers from Imperial College London, Oxford University and Nottingham University, together with 19 presentations from the University of Bradford, the University of Leeds, Sheffield University, Cranfield University, the University of Newcastle, the Denmark Technical University, and the University of Maribor.This special issue includes 8 contributions from the event that cover a number of important areas: energy, water, nano-materials, refinery planning and risk management, the application of knowledge based and artificial intelligence systems in manufacturing processes, and scheduling and optimisation.

In this work, emulsion copolymerization of Styrene and Methyl Methacrylate (MMA) is studied in batch and semi-batch process. A number of different dynamic optimization problems are formulated and solved for batch process; semi-batch process with and without pre-batch time. In the batch process, the overall monomer conversion (Xn) is maximized for different desired number average molecular weights (Mn). In semi-batch process without pre-batch time Mn is maximized. In semi-batch process with fixed pre-batch time at the beginning of the reaction process, Mn is maximized for different initial initiator concentration (CI0). A process model involving the equations for mass and energy balance, population balance describing the particle evolution in the reactor and diffusion controlled kinetics is incorporated into the optimization framework. The dynamic optimization problems are converted into nonlinear programming problem using the CVP techniques which are solved using efficient SQP (Successive Quadratic Programming) method available within the gPROMS (general PROcess Modelling System) software.

Energy consumption is a very important consideration for reducing environmental impact and maximizing the profitability of operations. Since high temperatures are employed in hydrotreating (HDT) processes, hot effluents can be used to heat other cold process streams. The aim of the present paper is to describe and analyze the heat integration (during hydrotreating of crude oil in trickle bed reactor) of a hydrotreating plant’s process based upon experimental work.In this work, crude oil is hydrotreated upon a commercial cobalt-molybdenum on alumina catalyst presulfided at specified conditions. Detailed pilot plant experiments are conducted in a continuous flow isothermal trickle bed reactor (TBR) in which the main hydrotreating reactions, are hydrodesulfurization (HDS), hydrodenitrogenation (HDN), hydrodeasphaltenization (HDAs) and hydrodemetallization (HDM). The latter includes hydrodevanadization (HDV) and hydrodenickelation (HDNi). The reaction temperature, the hydrogen pressure, and the liquid hourly space velocity (LHSV) are varied within certain ranges, with constant hydrogen to oil ratio (H2/Oil).Experimental information obtained from a pilot plant, together with kinetics and reactor modeling tools,and a commercial process data are employed for heat integration process model. The optimization problem to minimize the overall annual cost is formulated as a Non-Linear Programming (NLP) problem, which is solved using Successive Quadratic Programming (SQP) within gPROMS.

In the current challenging and volatile political and economic environment, the process industry is exposed to a high degree of uncertainty that renders the production planning task to be a risky and complex optimization problem requiring high computational expense. This work proposes a computationally-tractable optimization-based framework for risk management in midterm process planning under uncertainty. We employ stochastic programming to account for the uncertainty in which a scenario-based approach is used to approximate the underlying probability distribution of the uncertain parameters. The problem is formulated as a recourse-based two-stage stochastic program that incorporates a mean-risk structure in the objective function. Two risk measures are applied, namely Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). However, since a large number of scenarios are often required to capture the stochasticity of the problem, the model suffers from the curse of dimensionality. To circumvent this problem, we propose a computational procedure with a relatively low computational burden that involves the following two major steps. First, a linear programming (LP) approximation of the risk-inclined version of the planning model is solved for a number of randomly generated scenarios. Subsequently, the VaR parameters of the model are simulated and incorporated into a mean–CVaR stochastic LP approximation of the risk-averse version of the planning model. The proposed approach is implemented on a petroleum refinery planning case study with satisfactory results that demonstrate how solutions with relatively affordable computational expense can be attained in a risk-averse model in the face of uncertainty. Future work will mainly involve extending the approach to a multiobjective formulation as well as for mixed-integer optimization problems.

This paper presents using bootstrap aggregated neural networks for the modelling and optimisation control of reactive polymer composite moulding processes. Neural network models for the degree of cure are developed from process operational data. In order to improve model generalization capability, multiple neural networks are developed from bootstrap re-samples of the original data and are combined. Reliable optimal heating profiles are obtained by solving an optimisation problem using the bootstrap aggregated neural network model and incorporating model prediction confidence intervals in the optimisation objective function. The proposed method is applied to both simulated data and real industrial data.

Continuous hydrothermal flow synthesis (CHFS) systems can provide high quality fine nanoparticles. However, optimisation of the CHFS system including the reactor and heat exchanger design, and their scaling-up for commercial applications have not been studied and cannot be achieved only through laboratory and pilot plant experiments. CFD modelling techniques are being widely used to simulate fluid field, heat and mass transfer in a lot of industrial process equipment. However, the application of CFD to model CHFS systems is still rare. This paper employs CFD methodology to simulate fluid flow and heat transfer in a counter-current reactor and a tubular heat exchanger of a laboratory-scale CHFS system for the production of TiO2 nanoparticles. The distributions of flow and heat transfer variables such as velocity and temperature in both units are obtained using ANSYS Fluent package. The tracer concentration profile is also simulated via solving the species equations to investigate the mixing behaviour in the counter-current reactor. Temperature distributions at different locations in a counter-current reactor and a tubular heat exchanger of a CHFS system were obtained experimentally. The simulated temperatures in both the reactor and the heat exchanger are compared with the available experimental data, which reveals that a good level of agreement is achieved.

Global manufacturing industry mostly depends on new product development and processes to become competitive. The product development process for automotive industry is normally complicated, lengthy, expensive, and risky. Hence, a study of lean manufacturing processes for low volume manufacturing in automotive industry is proposed to overcome this issue by eliminating all wastes in the lengthy process. This paper presents a conceptual design approach to the development of a hybrid Knowledge Based (KB) system for lean process in Low Volume Automotive Manufacturing (LVAM). The research concentrates on the low volume processes by using a hybrid KB system, which is a blend of KB system and Gauging Absences of Pre-requisites (GAP). The hybrid KB/GAP system identifies all potential waste elements of low volume process manufacturing. The KB system analyses the difference between the existing and the benchmark standards for lean process for an effective implementation through the GAP analysis technique. The proposed model explores three major lean process components, namely Employee Involvement, Waste Elimination, and Kaizen (continuous improvement). These three components provide valuable information in order for decision makers to design and implement an optimised low volume manufacturing process, but which can be applied in all process manufacturing, including chemical processing.

Scheduling is an important element that has a major impact on the efficiency of all manufacturing processes. It plays an important role in optimising the manufacturing times and costs resulting in energy efficient processes. It has been estimated that more than 75% of manufacturing processes occur in small batches. In such environments, processes must be able to perform a variety of operations on a mix of different batches. Batch-job scheduling optimisation is the response to such low batch manufacturing problems. The optimisation of batch-job process scheduling problem is still a challenge to researchers and is far from being completely solved due to its combinatorial nature. In this paper, a novel hybrid heuristic (HybH) solution approach for batch-job scheduling problem is presented with the objective of optimising the overall Makespan (Cmax). The proposed HybH is the combination of Index Based Heuristic (IBH) and the Finished Batch-Job (FBJ) process schedule. The heuristic assigns the first operation to a batch-job using IBH and the remaining operations on the basis FBJ process schedule. The FBJ process schedule gives priority to the batch-job with early finished operations, without violating the constraints of process order. The proposed HybH is explained with the help of a detailed example. Several benchmark problems are solved from the literature to check the validity and effectiveness of the proposed heuristic. The presented HybH has achieved batch-job process schedules which have outperformed the traditional heuristics. The results are encouraging and show that the proposed heuristic is a valid methodology for batch process scheduling optimisation.

Desalination by reverse osmosis is receiving increasing attention due to recent improvements in membrane technology in particular. This technique is now used for small to very large scale applications, and has been identified as a key contributor to water treatment and provision not only in Middle Eastern countries but also closer to home in the United Kingdom for example. Computational Fluid Dynamics (CFD) is increasingly used for complex modelling applications in the chemical process and water industries. Previous work by the writers and others for spacer-filled desalination channels have yielded very good results in terms of modelling the flow and salt characteristics and history in the channels, as well as the overall performance of the membrane system. However, transient features imply that such simulations can be very costly and, if they can approximate experimental layouts with horizontal membranes, the investigation of a full desalination performance and operation remains a very distant prospect. CFD would therefore appear to be unsuited to industrial design at the present time. It is necessary to reduce the cost of the numerical simulations whilst maintaining high fidelity in key design parameters such as pressure loss, shear stress and salt concentration on the membrane, and through flow across the membrane for the method to be useful in design. A momentum loss approach based on a porous medium model is presented to this effect and benchmarked against detailed CFD results with success. It is shown to be capable of meeting key design parameters at a fraction of the full CFD cost.