Thermal modelling of manufacturing processes and HVAC systems

The two main energy consumers within a manufacturing plant are the HVAC systems and manufacturing processes. Studies have predominately looked at energy demand associated with manufacturing a single product or a production line, as well as analysis of energy use within a building, but little work has investigated the interaction between manufacturing processes and the surrounding building. Dynamic time based building energy simulation was used to determine the thermal behaviour of the manufacturing facility. The study establishes the importance of analysing manufacturing energy flows alongside that of the building in order to capture all thermal and energy flows. The relationship between the energy demand of HVAC systems with manufacturing productivity is determined. The use of the current degree-day method of building analysis was proven inappropriate for manufacturing facilities, due to such significant heat gains from manufacturing equipment, and impact of equipment on indoor conditions. The need for a proactive HVAC system based on manufacturing demand is introduced, allowing for control of the environment prior to significant temperature or humidity changes

The development of modelling tools to improve energy efficiency in manufacturing processes and systems

With increasing governmental pressures to reduce energy consumption, manufacturing companies are faced with the challenge of reducing energy consumption whilst maintaining or increasing profits and productivity. Computational modelling is a powerful tool for energy analysis within the manufacturing industry as an effective decision making technique in order to optimise throughput, effectively plan and manage operations, reduce bottlenecks and test various scenarios. This study reviewed methodologies and frameworks developed for analysing energy consumption on a machine process level. Multi-level holistic analysis allowing for consideration of individual machines, the manufacturing process chain and built environment, with both discrete event and continuous based simulation are also presented. The requirement of a complete, high accuracy computational model is highlighted in order to understand the interaction between all relevant material, energy and resource flows. Challenges associated with achieving a holistic simulation of the manufacturing facility with all relevant parameters is presented, along with areas for further development. Furthermore, the development of Industry 4.0 is reviewed, along with new and emerging technologies allowing for increased automation, connectivity and flexibility within manufacturing, as well as visual techniques to provide further understanding and clarity of manufacturing processes such as digital twins, virtual and augmented reality

Coupling simulation with artificial neural networks for the optimisation of HVAC controls in manufacturing environments

Manufacturing remains one of the most energy intensive sectors, additionally, the energy used within buildings for heating, ventilation and air conditioning (HVAC) is responsible for almost half of the UK's energy demand. Commonly, these are analysed in isolation from one another. Use of machine learning is gaining popularity due to its ability to solve non-linear problems with large data sets and little knowledge about relationships between parameters. Such models use relationships between inputs and outputs to make further predictions on unseen data, without requiring any understanding regarding the system, making them highly suited to dealing with the stochastic data sets found in a manufacturing environment. This has been seen in literature for determining electrical energy demand for residential or commercial buildings, rather than manufacturing environments. This study proposes a novel method of coupling simulation with machine learning to predict indoor workshop conditions and building energy demand, in response to production schedules, outdoor conditions, building behaviour and use. Such predictions can subsequently allow for more efficient management of HVAC systems. Based upon predicted energy consumption, potential spikes were identified and manufacturing schedules subsequently optimised to reduce peak energy demand. Coupling simulation techniques with machine learning algorithms eliminates the requirement for costly and intrusive methods of data collection, providing a method of predicting and optimising building energy consumption in the manufacturing sector

Deep learning techniques for energy forecasting and condition monitoring in the manufacturing sector

The industrial and building sector demands the largest proportion of global energy, therefore adopting energy efficiency related strategies, optimization techniques and management is an important step towards global energy reduction. The use of machine learning techniques in energy forecasting is gaining popularity due to their ability to solve complex non-linear problems, however this is predominately seen in the residential and commercial sector. This study proposes and compares the use of two deep neural networks, feed forward and recurrent, to forecast manufacturing facility energy consumption and workshop conditions based on production schedules, climatic conditions, thermal properties of the facility building, along with building behaviour and use. The feed forward model was able to predict building energy, workshop air temperatures and humidity to an accuracy of 92.4%, 99.5% and 64.8% respectively when the model was provided with a new dataset, with the recurrent model predicting these variables to accuracies of 96.82%, 99.40% and 57.60%. The neural networks were trained with data obtained from the simulation of a medium sized manufacturing facility in the UK. Coupling simulation techniques with machine learning algorithms allows for a low cost, non-intrusive methodology of predicting and optimising building energy consumption in the manufacturing sector. Furthermore, the use of neural networks provided forecasted building energy profiles for the identification of spikes in energy consumption; an undesirable and considerable cost in the manufacturing sector, as well as the predication of manufacturing environmental conditions for condition monitoring of condition sensitive production environments

Coupling simulation with machine learning for the development of a proactive HVAC system in the manufacturing sector

The industrial sector consumes 55% of the world's energy consumption [1]. Following manufacturing processes, the HVAC system is the second largest energy consumer in manufacturing facilities, yet is generally uncounted for and considered an indirect cost to maintain a facility [2]. Any current efforts at reducing energy demand in the manufacturing sector have been focused towards process machines rather than on the manufacturing building as a holistic energy system. Currently, HVAC systems are reactive, responding to changes to the environment as they happen, based upon requirements for thermal comfort. Manufacturing facility environments however are subject to complex interactions between machine level resources, water, heat and compressed air.;This study questions the suitability of the reactive thermal comfort based HVAC system, and proposes a proactive manufacturing based HVAC control system, utilising predicted optimum HVAC set points. Through the use of simulation, a holistic analysis of a manufacturing facility was performed, based on building location and layout, building fabrics, weather conditions and manufacturing demand in order to determine the relationship between manufacturing demand and HVAC control. A number of predictive models were analysed for suitability for use in the manufacturing, before being trained on simulation data for the prediction of optimum HVAC set points and corresponding facility indoor conditions.;Simulation was coupled with predictive modelling in order to predict building energy and HVAC energy demand, allowing for the identification of potential future spikes in consumption, followed by subsequent HVAC and manufacturing schedule optimisation, allowing for a 15.1 % reduction in peak energy demand. Through simulation and predictive modelling, the research has demonstrated the potential energy savings achieved by adopting a proactive HVAC system in the manufacturing sector. Such a methodology achieved 14.1 % energy savings over a 12-month period for an analysed case study environment. [See thesis text for references]The industrial sector consumes 55% of the world's energy consumption [1]. Following manufacturing processes, the HVAC system is the second largest energy consumer in manufacturing facilities, yet is generally uncounted for and considered an indirect cost to maintain a facility [2]. Any current efforts at reducing energy demand in the manufacturing sector have been focused towards process machines rather than on the manufacturing building as a holistic energy system. Currently, HVAC systems are reactive, responding to changes to the environment as they happen, based upon requirements for thermal comfort. Manufacturing facility environments however are subject to complex interactions between machine level resources, water, heat and compressed air.;This study questions the suitability of the reactive thermal comfort based HVAC system, and proposes a proactive manufacturing based HVAC control system, utilising predicted optimum HVAC set points. Through the use of simulation, a holistic analysis of a manufacturing facility was performed, based on building location and layout, building fabrics, weather conditions and manufacturing demand in order to determine the relationship between manufacturing demand and HVAC control. A number of predictive models were analysed for suitability for use in the manufacturing, before being trained on simulation data for the prediction of optimum HVAC set points and corresponding facility indoor conditions.;Simulation was coupled with predictive modelling in order to predict building energy and HVAC energy demand, allowing for the identification of potential future spikes in consumption, followed by subsequent HVAC and manufacturing schedule optimisation, allowing for a 15.1 % reduction in peak energy demand. Through simulation and predictive modelling, the research has demonstrated the potential energy savings achieved by adopting a proactive HVAC system in the manufacturing sector. Such a methodology achieved 14.1 % energy savings over a 12-month period for an analysed case study environment. [See thesis text for references