Discrete event modelling for evaluation and optimisation of power utility energy demand

Purpose: The cost and environmental impact of energy is driving better quantification of energy utilization in a business context. Determining an entire business electrical energy usage, inclusive of core operations and support activities, in a singular evaluation protocol is a challenge. The challenge is exasperated when changes occur in the business, where every change implies significant rework of the business energy calculations. This study develops a holistic energy determination model for the entire business requiring minimum inputs for energy re-calculation, when aspects of the business changes. Design/methodology/approach: The research adopts a quantitative approach enabled through a Discrete Event Model. The model is developed based on the activities performed in every functional area of the business. The activities are captured using business process science. The processes are then developed into a DES Model. The model development cycle includes data collection, model development and configuration, model validation and scenario models for optimization. Findings: A coal fired power generation business, with multiple sites is comprehensively simulated to evaluate the baseline electrical energy demand and associated CO2 emissions. The results are captured at various levels of the business including; Enterprise; site, business function and equipment level. The generation sites operational functions are identified as major electrical energy consumers. The adoption of Industry 4.0 technologies of Internet of Things, Big Data Analytics, mobility and automation demonstrate energy savings of 1% of total site demand. As the Industry 4.0 technologies are applied to a limited number of processes, the results demonstrate the capability of these technologies having a significant impact on electrical energy demand and CO2 emission when applied to a broader spectrum of business processes. Research limitations/implications: The research is limited to a multi-site energy generating company, which is a coal to energy business. Practical implications: The research has significant practical implications, mostly on the mechanisms to evaluate business energy utilisation. The ability to include all areas of the business is a key practical differentiator, as compared to traditional models focusing on operations only. Originality/value: The model is unique in that it is a model that is system agnostic to any production configuration, most especially changes in configuration. This implies that the model can be easily and quickly adapted with changes in the business. This implies the model proposed would be significantly more adaptable when compared to traditional approachesPeer Reviewe

Hospital energy demand forecasting for prioritisation during periods of constrained supply

Purpose: Sustaining healthcare operations without adequate energy capacity creates significant challenges, especially during periods of constrained energy supply. This research develops a clinical and non-clinical activity-based hospital energy model for electrical load prioritization during periods of constrained energy supply. Design/methodology/approach: Discrete event modelling is adopted for development of the hospital energy model (HEM). The building block of the HEM is business process mapping of a hospitals clinical and non-clinical activities. The model prioritizes the electrical load demand as Priority 1, 2 and 3; Priority 1 activities are essential to the survival of patients, Priority 2 activities are critical activities that are required after one to four hours, and Priority 3 activities can run for several hours without electricity. Findings: The model was applied to small, medium, and large hospitals. The results demonstrate that Priority 2 activities have the highest energy demand, followed by Priority 1 and Priority 3 activities, respectively for all hospital sizes. For the medium and large hospitals, the top three contributors to energy demand are lighting, HVAC, and patient services. For the small hospital, it is patient services, lighting, and HVAC. Research limitations/implications: The model is specific to hospitals but can be modified for other healthcare facilities. Practical implications: The resolution of the electrical energy demand down to the business activity level enables hospitals to evaluate current practices for optimization. It facilitates multiple energy supply scenarios, enabling hospital management to conduct feasibility studies based on available power supply options Social implications: Improved planning of capital expenditure and operational budgets. Improved operations during periods of constrained energy supply, which reduces the risk to hospitals and ensures consistent quality of service. Originality/value: Current hospital energy models are limited, especially for operations management under constrained energy supply. A simple to use model is proposed to assist in planning of activities based on available supplyPeer Reviewe

Industry 4.0 cyber physical model for energy optimisation

Abstract : Please refer to full text to view abstract.D.Phil. (Engineering Management

Industry 4.0 Technological Advancement in the Food and Beverage Manufacturing Industry in South Africa—Bibliometric Analysis via Natural Language Processing

The food and beverage (FOODBEV) manufacturing industry is a significant contributor to global economic development, but it is also subject to major global competition. Manufacturing technology evolution is rapid and, with the Fourth Industrial Revolution (4IR), ever accelerating. Thus, the ability of companies to review and identify appropriate, beneficial technologies and forecast the skills required is a challenge. 4IR technologies, as a collection of tools to assist technological advancement in the manufacturing sector, are essential. The vast and diverse global technology knowledge base, together with the complexities associated with screening in technologies and the lack of appropriate enablement skills, makes technology selection and implementation a challenge. This challenge is premised on the knowledge that there are vast amounts of information available on various research databases and web search engines; however, the extraction of specific and relevant information is time-intensive. Whilst existing techniques such as conventional bibliometric analysis are available, there is a need for dynamic approaches that optimise the ability to acquire the relevant information or knowledge within a short period with minimum effort. This research study adopts smart knowledge management together with artificial intelligence (AI) for knowledge extraction, classification, and adoption. This research defines 18 FOODBEV manufacturing processes and adopts a two-tier Natural Language Processing (NLP) protocol to identify technological substitution for process optimisation and the associated skills required in the FOODBEV manufacturing sector in South Africa