Zhigang Shang’s research while affiliated with Cranfield University and other places

This paper presents an agent-based intelligent system to support coordinate manufacturing execution and decision-making in chemical process industry. A multi-agent system (MAS) framework is developed to provide a flexible infrastructure for the integration of chemical process information and process models. The system comprise of a process knowledge base and a group of functional agents. Agents in the system can communicate and cooperate with each other to exchange and share information, and to achieve timely decisions in dealing with various scenarios in process operations and manufacturing management. Process simulation, artificial intelligent technique, rule-based decision supports are integrated in this system for process analysis, process monitoring, process performance prediction and operation suggestion. The implementation of this agent-based system was illustrated with two case studies, including one application in process monitoring and process performance prediction for a chemical process and one application in de-bottlenecking of a site utility system.

Energy utility systems are typically responsible for satisfying internal customers (e.g., the various process plants in the industrial complex). The increasing independence of business units in the complex matches an emerging trend in the utility systems to operate for own economic viability and for the encouragement to trade with both internal and external customers. The paper presents a dynamic management system supporting autonomy and the optimal operation of the utility system. The management system comprises three functional components, which support negotiation, short-term (tactical) and long-term (strategic) optimisation. The negotiation component involves an agent-based system exploiting the knowledge base established with real-time and historical data, whereas the optimisation provides a primal front (operational changes) and background front (structural changes) to account for the tactical and strategic decisions.

Large amounts of gaseous emissions are generated by combustion processes associated with the utility systems. The emissions include SOx, CO2, CO, NOx, CH4, and N2O. Such emissions can result in significant impact on the surrounding environment. As a result of serious concerns about environmental problems in recent years, the design criteria for a modern utility system should include both environmental and economic requirements. This work proposes a multi-objective optimisation (MOO) strategy to identify the sustainable design of utility systems that satisfies both economic and environmental goals. A MOO mixed integer linear programming (MILP) model is developed to combine the minimisation of costs with the minimisation of environmental impact that is assessed in terms of life cycle environmental burdens. Most of the gaseous emissions are addressed in the model. The resulting MOO problem is solved using lexicographic goal programming (LGP) techniques. The new strategy has been applied to a case study for the design of a utility system with specific utility demands.

The paper presents a systematic methodology for the optimal design and operational management of offshore oil fields. It is comprised of two stages. At the design stage, the optimal production capacity of a main field is determined with an adjacent satellite field and a well drilling schedule. The problem is formulated as a mixed-integer linear programming formulation. Continuous variables represent individual well, jacket and topsides costs. Binary variables are used to select individual wells within a defined field grid. The mathematical formulation is concise and efficient. An MINLP model is proposed for the operational management optimisation of the offshore oilfields. In the latter model, non-linear equations are extensively used to model the pressure drops in pipes and wells for multiphase flow. Non-linear cost equations have been derived for the production costs of each well accounting for the length, the production rate and their maintenance. Operational decisions determine the oil flowrates, the operation/shut-in for each well and the pressures for each point in the piping network.

The paper presents an agent-enabled environment to support decisions and the dynamic trading of utility services. The developments are set up to emulate different departments of a total site, individual production processes, the utility system, and trading departments. The proposed approach reviews ways to assess investment schemes, as well as design and operational scenarios. The agents make use of knowledge models that communicate with individual processes and assess scenarios for energy efficiency. Optimization models take into account objectives for improvements, whereas agents take into account the dynamics of the communication. Knowledge and optimization models are linked with databases that contain planning and operational data useful to manage and support decisions. The approach is illustrated with two case studies.

In this paper, a systematic methodology has been presented for the optimal integration of the IGCC systems with the exiting refinery infrastructure, the hydrogen network and the utility network. The proposed approach determines the new unit investment scheme for the existing refineries, as well as operational strategies of all the units in order to maximise the annual profit taking into account the annualised capital cost. The proposed methodology follows a two-stage procedure, namely analysis stage and optimisation stage. The analysis stage screens among various integration alternatives and identify the promising options considering the trade-off between the economic and environmental benefits and the capital expenditure. Because only economically viable options are included, the superstructure will be potentially much smaller than a general superstructure which includes all the candidate options. With a reduced superstructure, the mathematical optimisation problem is formulated as an NLP model which can be solved effectively even for large problems.

The paper presents a systematic approach for the synthesis of flexible utility systems satisfying varying energy demands. The approach combines benefits of total site analysis, thermodynamic analysis and mathematical optimisation. A thermodynamic efficiency curve (TEC) is developed, which gives an overview of the maximum thermodynamic efficiencies of all possible design alternatives. TEC and hardware composites guide the selection of candidate structures in the superstructure, excluding uneconomic options from the synthesis model. The integration of thermodynamics yields significant reduction in the synthesis model, addresses the impact of variable loads on the unit efficiencies, and enables a compact formulation of the design problem over long horizons of operation. The optimisation is formulated as a multi-period MILP problem that relies on new target models to describe the performance of steam turbines, condensing turbines, gas turbines and boilers. Target models account for the variation of efficiency with unit size, load and operating conditions in a simple, yet accurate way. As a result, these models are capable of accounting for the efficiency trends of realistic units.

A new approach for the optimisation of steam levels of total site utility systems satisfying varying utility demands is presented, accounting for interactions between total site utility systems and chemical processes. The optimisation problem involves the selection of the steam levels with respect to their temperatures. In this paper, by exploiting total site analysis techniques, a new transhipment network is developed to represent the heat flows of a total site. Base on the transhipment network representation of the total site, a general multi-period mixed-integer linear programming (MILP) model is presented for identifying the optimal steam levels of the total site utility system. By using engineering and thermodynamic knowledge, a boiler hardware model (BHM) is developed to describe the performance of boilers, and the turbine hardware model (THM) is applied for the shaft-work targeting of steam turbines. Both models are capable of predicting the real efficiency trends of the units. The application of the proposed optimisation approach is illustrated through two case studies including single operation scenario and multiple operation scenarios.

A new approach for the optimisation of steam levels of total site utility systems satisfying varying utility demands is presented, accounting for interactions between total site utility systems and chemical processes. The optimisation problem involves the selection of the steam levels with respect to their temperatures. In this paper, by exploiting total site analysis techniques, a new transhipment network is developed to represent the heat flows of a total site. Base on the transhipment network representation of the total site, a general multi-period mixed-integer linear programming (MILP) model is presented for identifying the optimal steam levels of the total site utility system. By using engineering and thermodynamic knowledge, a boiler hardware model (BHM) is developed to describe the performance of boilers, and the turbine hardware model (THM) is applied for the shaft-work targeting of steam turbines. Both models are capable of predicting the real efficiency trends of the units. The application of the proposed optimisation approach is illustrated through two case studies including single operation scenario and multiple operation scenarios.

The proper design criteria for a modern utility plant should include both environmental and economic requirements. In other words, not only the capital and operating costs of a utility plant but also the corresponding utility wastes must be minimised. The paper presents a systematic multicriteria process synthesis approach for designing sustainable and economic utility systems. The proposed approach enables the design engineer to systematically derive optimal utility systems which are economically sustainable and economic by embedding Life Cycle Assessment (LCA) principles within a multiple objective optimisation framework. It combines the merits of total site analysis, LCA, and multi-objective optimisation techniques