The Virtual Interactive Relationship Between BIM Project Teams: Effective Communication to aid Collaboration in the Design Process

Building Information Modelling (BIM) provides a shared source of information about a built asset, which creates a collaborative virtual environment for project teams. Literature suggests that to collaborate efficiently, the relationship between the project team is based on sympathy, obligation, trust and rapport. Communication increases in importance when working collaboratively but effective communication can only be achieved when the stakeholders are willing to act, react, listen and share information. Case study research and interviews with Architecture, Engineering and Construction (AEC) industry experts suggest that synchronous face-to-face communication is project teams’ preferred method, allowing teams to socialise and build rapport, accelerating the creation of trust between the stakeholders. However, virtual unified communication platforms are a close second-preferred option for communication between the teams. Effective methods for virtual communication in professional practice, such as virtual collaboration environments (CVE), that build trust and achieve similar spontaneous responses as face-to-face communication, are necessary to face the global challenges and can be achieved with the right people, processes and technology. This research paper investigates current industry methods for virtual communication within BIM projects and explores the suitability of avatar interaction in a collaborative virtual environment as an alternative to face-to-face communication to enhance collaboration between design teams’ professional practice on a project. Hence, this paper presents comparisons between the effectiveness of these communication methods within construction design teams with results of further experiments conducted to test recommendations for more efficient methods for virtual communication to add value in the workplace between design teams.

A new technique for recovering energy in thermally coupled distillation using vapor recompression cycles

Even though it has been proved that a fully thermally coupled distillation (TCD) system minimizes the energy used by a sequence of columns, it is well-known that vapor/liquid transfers between different sections produce an unavoidable excess of vapor (liquid) in some of them, increasing both the investment and operating costs. It is proposed here to take advantage of this situation by extracting the extra vapor/liquid and subjecting it to a direct/reverse vapor compression cycle. This new arrangement restores the optimal operating conditions of some of the affected sections with energy savings of around 20–30% compared with conventional TCD columns. Various examples, including the direct and reverse vapor recompression cycles, are presented. Furthermore, in each example, all possible modes of distillation (direct, indirect and Petlyuk distillation) with and without vapor recompression cycles (VRC) are compared to ensure that this approach delivers the best results.

Combined simulation-optimization methodology for the design of environmental conscious absorption systems

This work addresses the optimization of ammonia–water absorption cycles for cooling and refrigeration applications with economic and environmental concerns. Our approach combines the capabilities of process simulation, multi-objective optimization (MOO), cost analysis and life cycle assessment (LCA). The optimization task is posed in mathematical terms as a multi-objective mixed-integer nonlinear program (moMINLP) that seeks to minimize the total annualized cost and environmental impact of the cycle. This moMINLP is solved by an outer-approximation strategy that iterates between primal nonlinear programming (NLP) subproblems with fixed binaries and a tailored mixed-integer linear programming (MILP) model. The capabilities of our approach are illustrated through its application to an ammonia–water absorption cycle used in cooling and refrigeration applications.

GE models and algorithms for condensed phase equilibrium data regression in ternary systems: limitations and proposals

Phase equilibrium data regression is an unavoidable task necessary to obtain the appropriate values for any model to be used in separation equipment design for chemical process simulation and optimization. The accuracy of this process depends on different factors such as the experimental data quality, the selected model and the calculation algorithm. The present paper summarizes the results and conclusions achieved in our research on the capabilities and limitations of the existing GE models and about strategies that can be included in the correlation algorithms to improve the convergence and avoid inconsistencies. The NRTL model has been selected as a representative local composition model. New capabilities of this model, but also several relevant limitations, have been identified and some examples of the application of a modified NRTL equation have been discussed. Furthermore, a regression algorithm has been developed that allows for the advisable simultaneous regression of all the condensed phase equilibrium regions that are present in ternary systems at constant T and P. It includes specific strategies designed to avoid some of the pitfalls frequently found in commercial regression tools for phase equilibrium calculations. Most of the proposed strategies are based on the geometrical interpretation of the lowest common tangent plane equilibrium criterion, which allows an unambiguous comprehension of the behavior of the mixtures. The paper aims to show all the work as a whole in order to reveal the necessary efforts that must be devoted to overcome the difficulties that still exist in the phase equilibrium data regression problem.

3D teaching: the ideal complement for professionals in design and construction

Today, the requirement of professional skills to university students is constantly increasing in our society. In our opinion, the content offered in official degrees need to be nourished with different variables, enriching their global professional knowledge in a parallel way; that is why, in recent years, there is a great multiplicity of complementary courses at university. One of the most socially demanded technical requirements within the architectural, design or engineering field is the management of 3D drawing software, becoming an indispensable reality in these sectors. Thus, this specific training becomes essential over two-dimension traditional design, because the inclusion of great possibilities of spatial development that go beyond conventional orthographic projections (plans, sections or elevations), allowing modelling and rotation of the selected items from multiple angles and perspectives. Therefore, this paper analyzes the teaching methodology of a complementary course for those technicians in the construction industry interested in computer-aided design, using modelling (SketchupMake) and rendering programs (Kerkythea). The course is developed from the technician point of view, by learning computer management and its application to professional development from a more general to a more specific view through practical examples. The proposed methodology is based on the development of real examples in different professional environments such as rehabilitation, new constructions, opening projects or architectural design. This multidisciplinary contribution improves criticism of students in different areas, encouraging new learning strategies and the independent development of three-dimensional solutions. Thus, the practical implementation of new situations, even suggested by the students themselves, ensures active participation, saving time during the design process and the increase of effectiveness when generating elements which may be represented, moved or virtually tested. In conclusion, this teaching-learning methodology improves the skills and competencies of students to face the growing professional demands of society. After finishing the course, technicians not only improved their expertise in the field of drawing but they also enhanced their capacity for spatial vision; both essential qualities in these sectors that can be applied to their professional development with great success.

Large scale optimization of a sour water stripping plant using surrogate models

In this work, we propose a new methodology for the large scale optimization and process integration of complex chemical processes that have been simulated using modular chemical process simulators. Units with significant numerical noise or large CPU times are substituted by surrogate models based on Kriging interpolation. Using a degree of freedom analysis, some of those units can be aggregated into a single unit to reduce the complexity of the resulting model. As a result, we solve a hybrid simulation-optimization model formed by units in the original flowsheet, Kriging models, and explicit equations. We present a case study of the optimization of a sour water stripping plant in which we simultaneously consider economics, heat integration and environmental impact using the ReCiPe indicator, which incorporates the recent advances made in Life Cycle Assessment (LCA). The optimization strategy guarantees the convergence to a local optimum inside the tolerance of the numerical noise.

A novel hybrid simulation-optimization approach for the optimal design of multicomponent distillation columns

Póster presentado en Escape 22, European Symposium on Computer Aided Process Engineering, University College London, UK, 17-20 June 2012.

Some examples of modeling in chemical engineering: thermal treatment of polymers, phase equilibrium calculations and process design

Presentation submitted to PSE Seminar, Chemical Engineering Department, Center for Advanced Process Design-making (CAPD), Carnegie Mellon University, Pittsburgh (USA), October 2012.

A hybrid simulation-optimization approach for the design of internally heat-integrated distillation columns

Paper submitted to AIChE 2012 Annual Meeting: Energy Efficiency by Process Intensification, Pittsburgh, PA, October 28-November 2, 2012.

Hybrid simulation-optimization based approach for the optimal design of single-product biotechnological processes

In this work, we present a systematic method for the optimal development of bioprocesses that relies on the combined use of simulation packages and optimization tools. One of the main advantages of our method is that it allows for the simultaneous optimization of all the individual components of a bioprocess, including the main upstream and downstream units. The design task is mathematically formulated as a mixed-integer dynamic optimization (MIDO) problem, which is solved by a decomposition method that iterates between primal and master sub-problems. The primal dynamic optimization problem optimizes the operating conditions, bioreactor kinetics and equipment sizes, whereas the master levels entails the solution of a tailored mixed-integer linear programming (MILP) model that decides on the values of the integer variables (i.e., number of equipments in parallel and topological decisions). The dynamic optimization primal sub-problems are solved via a sequential approach that integrates the process simulator SuperPro Designer® with an external NLP solver implemented in Matlab®. The capabilities of the proposed methodology are illustrated through its application to a typical fermentation process and to the production of the amino acid L-lysine.

Logic hybrid simulation-optimization algorithm for distillation design

In this paper, we propose a novel algorithm for the rigorous design of distillation columns that integrates a process simulator in a generalized disjunctive programming formulation. The optimal distillation column, or column sequence, is obtained by selecting, for each column section, among a set of column sections with different number of theoretical trays. The selection of thermodynamic models, properties estimation etc., are all in the simulation environment. All the numerical issues related to the convergence of distillation columns (or column sections) are also maintained in the simulation environment. The model is formulated as a Generalized Disjunctive Programming (GDP) problem and solved using the logic based outer approximation algorithm without MINLP reformulation. Some examples involving from a single column to thermally coupled sequence or extractive distillation shows the performance of the new algorithm.

Integration of different models in the design of chemical processes: Application to the design of a power plant

With advances in the synthesis and design of chemical processes there is an increasing need for more complex mathematical models with which to screen the alternatives that constitute accurate and reliable process models. Despite the wide availability of sophisticated tools for simulation, optimization and synthesis of chemical processes, the user is frequently interested in using the ‘best available model’. However, in practice, these models are usually little more than a black box with a rigid input–output structure. In this paper we propose to tackle all these models using generalized disjunctive programming to capture the numerical characteristics of each model (in equation form, modular, noisy, etc.) and to deal with each of them according to their individual characteristics. The result is a hybrid modular–equation based approach that allows synthesizing complex processes using different models in a robust and reliable way. The capabilities of the proposed approach are discussed with a case study: the design of a utility system power plant that has been decomposed into its constitutive elements, each treated differently numerically. And finally, numerical results and conclusions are presented.

Rigorous Design of Complex Distillation Columns Using Process Simulators and the Particle Swarm Optimization Algorithm

We present a derivative-free optimization algorithm coupled with a chemical process simulator for the optimal design of individual and complex distillation processes using a rigorous tray-by-tray model. The proposed approach serves as an alternative tool to the various models based on nonlinear programming (NLP) or mixed-integer nonlinear programming (MINLP) . This is accomplished by combining the advantages of using a commercial process simulator (Aspen Hysys), including especially suited numerical methods developed for the convergence of distillation columns, with the benefits of the particle swarm optimization (PSO) metaheuristic algorithm, which does not require gradient information and has the ability to escape from local optima. Our method inherits the superstructure developed in Yeomans, H.; Grossmann, I. E.Optimal design of complex distillation columns using rigorous tray-by-tray disjunctive programming models. Ind. Eng. Chem. Res.2000, 39 (11), 4326–4335, in which the nonexisting trays are considered as simple bypasses of liquid and vapor flows. The implemented tool provides the optimal configuration of distillation column systems, which includes continuous and discrete variables, through the minimization of the total annual cost (TAC). The robustness and flexibility of the method is proven through the successful design and synthesis of three distillation systems of increasing complexity.

Simultaneous environmental and economic process synthesis of Isobutane Alkylation

This multidisciplinary study concerns the optimal design of processes with a view to both maximizing profit and minimizing environmental impacts. This can be achieved by a combination of traditional chemical process design methods, measurements of environmental impacts and advanced mathematical optimization techniques. More to the point, this paper presents a hybrid simulation-multiobjective optimization approach that at once optimizes the production cost and minimizes the associated environmental impacts of isobutane alkylation. This approach has also made it possible to obtain the flowsheet configurations and process variables that are needed to manufacture isooctane in a way that satisfies the above-stated double aim. The problem is formulated as a Generalized Disjunctive Programming problem and solved using state-of-the-art logic-based algorithms. It is shown, starting from existing alternatives for the process, that it is possible to systematically generate a superstructure that includes alternatives not previously considered. The optimal solution, in the form a Pareto curve, includes different structural alternatives from which the most suitable design can be selected. To evaluate the environmental impact, Life Cycle Assessment based on two different indicators is employed: Ecoindicator 99 and Global Warming Potential.

Isobutane Alkylation Process Synthesis by means of Hybrid Simulation-Multiobjective Optimization

Multiobjective Generalized Disjunctive Programming (MO-GDP) optimization has been used for the synthesis of an important industrial process, isobutane alkylation. The two objective functions to be simultaneously optimized are the environmental impact, determined by means of LCA (Life Cycle Assessment), and the economic potential of the process. The main reason for including the minimization of the environmental impact in the optimization process is the widespread environmental concern by the general public. For the resolution of the problem we employed a hybrid simulation- optimization methodology, i.e., the superstructure of the process was developed directly in a chemical process simulator connected to a state of the art optimizer. The model was formulated as a GDP and solved using a logic algorithm that avoids the reformulation as MINLP -Mixed Integer Non Linear Programming-. Our research gave us Pareto curves compounded by three different configurations where the LCA has been assessed by two different parameters: global warming potential and ecoindicator-99.