Another side of using load cell to measure the viscosity of a cookie jam for home industries with the ATMega328 for monitoring purposes

A simple structure to measure the viscosity of a cookie jam has been built. The purpose is to help home industries to judge the right time to stop heating and stirring. The value is used in order to get the optimum result of jam for a good taste and economically considered. The load cell is applied to measure the viscosity by fixing it to the stirrer wing and converting the viscosity directly as weight. The number of the viscosity is compared to the OIL SAE commercial standard number and programmed in the microcontroller to display the corresponding values as approximation. The structure and the system can be easily adapted for other kind of purposes with similar interes

Development of a hybrid metaheuristic for the efficient solution of strategic supply chain management problems: application to the energy sector

Supply chain management (SCM) addresses the strategic, tactical, and operational decision making that optimizes the supply chain performance. The strategic level defines the supply chain configuration: the selection of suppliers, transportation routes, manufacturing facilities, production levels, technologies. The tactical level plans and schedules the supply chain to meet actual demand. The operational level executes plans. Tactical and operational level decision-making functions are distributed across the supply chain. To increase or optimize performance, supply-chain functions must be perfectly coordinated. But the cycles of the enterprise and the market make this difficult: raw material does not arrive on time, production facilities fail, workers are ill, customers change or cancel orders, therefore, causing deviations from the plan. In some cases, these situations may be dealt with locally. In other cases, the problem cannot be ”locally contained” and modifications across many functions are required. Consequently, the supply chain management system must coordinate the revision of plans or schedules. The ability to better understand an algorithm is important to focus on the following variables: tactical and operational levels of the supply chain so that the timely dissemination of information, accurate coordination of decisions, and management of actions among people and systems is achieved ultimately determines the efficient, coordinated achievement of enterprise goal

Coordinating industrial production and cogeneration systems to exploit electricity price fluctuations

Las fluctuaciones en el precio de la electricidad, procedentes de la aplicación de programas de respuesta de la demanda, son una oportunidad para que las industrias que cuenten con sistemas de cogeneración puedan reducir sus costes de producción mientras hacen que la red eléctrica sea más estable y segura en su conjunto. Dada la cantidad de factores involucrados y la dificultad que esto supone a la hora de tomar decisiones, en esta tesis se presenta una metodología basada en optimización dinámica que permite la gestión óptima de ambos sistemas y se aplica en simulación al caso de estudio de una industria azucarera. Como principales resultados, se ha obtenido que utilizando la metodología propuesta los costes variables de producción se pueden reducir hasta un 2.55% si se utiliza una tarifa por tramos típica, y en torno a un 5.41% si se utilizan los precios dados por el mercado eléctrico directamente.Departamento de Ingeniería de Sistemas y AutomáticaDoctorado en Ingeniería Industria

Development of a hybrid metaheuristic for the efficient solution of strategic supply chain management problems: application to the energy sector

Supply chain management (SCM) addresses the strategic, tactical, and operational decision making that optimizes the supply chain performance. The strategic level defines the supply chain configuration: the selection of suppliers, transportation routes, manufacturing facilities, production levels, technologies. The tactical level plans and schedules the supply chain to meet actual demand. The operational level executes plans. Tactical and operational level decision-making functions are distributed across the supply chain. To increase or optimize performance, supply-chain functions must be perfectly coordinated. But the cycles of the enterprise and the market make this difficult: raw material does not arrive on time, production facilities fail, workers are ill, customers change or cancel orders, therefore, causing deviations from the plan. In some cases, these situations may be dealt with locally. In other cases, the problem cannot be ”locally contained” and modifications across many functions are required. Consequently, the supply chain management system must coordinate the revision of plans or schedules. The ability to better understand an algorithm is important to focus on the following variables: tactical and operational levels of the supply chain so that the timely dissemination of information, accurate coordination of decisions, and management of actions among people and systems is achieved ultimately determines the efficient, coordinated achievement of enterprise goal

A knowledge based supervisory support system for pan stage operations in a sugar mill

The recent downturn in world sugar prices has placed even greater demands upon the Australian sugar industry to reduce the costs of sugar manufacture and increase the consistency of producing high quality sugar. One of the proposed approaches in increasing the consistency of very high quality sugar production and leveraging further avenues for cost saving is in the development of a computer based advisory system. This system is able to provide expert knowledge in the area of pan stage management and best practices in the absence of human experts. This thesis explores the design, key features and outcomes of a knowledge based supervisory support system (KBSSS) framework proposed specifically for providing cooperative decision support in the area of pan stage operations within a sugar mill. To demonstrate the viability of the proposed KBSSS framework a prototype system was develop ed in accordance with the proposed framework. The KBSSS utilises three core innovative system technologies that form the core components of the proposed KBSSS framework. These technologies are: 1) Dynamic industrial pan stage process models for identifying the dynamic relationships between sections of pan stage operations to allow for future forecasting of pan stage operating conditions, 2) Integration techniques for the merging of the developed pan stage process models into the hybrid fuzzy logic expert system rule base to provide localisation adjustment to match with local real world factory operational conditions, and 3) Explanatory capabilities to provide justification and support of system advice and recommendations. As a result of research and development carried out in this thesis, the KBSSS's test results demonstrated in the thesis indicate the viability of the proposed KBSSS framework and highlight the forecasting capabilities of the developed system resulting in favourable outcomes compared to data from pan stage operations. As a result of the research undertaken in the thesis a prototype KBSSS, for pan stage operations, based upon the three core supporting intelligent system technologies reported in the thesis has been developed

A Comprehensive Optimization Framework for Designing Sustainable Renewable Energy Production Systems

As the world has recognized the importance of diversifying its energy resource portfolio away from fossil resources and more towards renewable resources such as biomass, there arises a need for developing strategies which can design renewable sustainable value chains that can be scaled up efficiently and provide tangible net environmental benefits from energy utilization. The objective of this research is to develop and implement a novel decision-making framework for the optimal design of renewable energy systems. The proposed optimization framework is based on a distributed, systematic approach which is composed of different layers including systems-based strategic optimization, detailed mechanistic modeling and operational level optimization. In the strategic optimization the model is represented by equations which describe physical flows of materials across the system nodes and financial flows that result from the system design and material movements. Market uncertainty is also incorporated into the model through stochastic programming. The output of the model includes optimal design of production capacity of the plant for the planning horizon by maximizing the net present value (NPV). The second stage consists of three main steps including simulation of the process in the simulation software, identification of critical sources of uncertainties through global sensitivity analysis, and employing stochastic optimization methodologies to optimize the operating condition of the plant under uncertainty. To exemplify the efficacy of the proposed framework a hypothetical lignocellulosic biorefinery based on sugar conversion platform that converts biomass to value-added biofuels and biobased chemicals is utilized as a case study. Furthermore, alternative technology options and possible process integrations in each section of the plant are analysed by exploiting the advantages of process simulation and the novel hybrid optimization framework. In conjunction with the simulation and optimization studies, the proposed framework develops quantitative metrics to associate economic values with technical barriers. The outcome of this work is a new distributed decision support framework which is intended to help economic development agencies, as well as policy makers in the renewable energy enterprises

Techno-economic evaluation of integrated process flowsheets for vinasse management with value addition for decision making

Bioethanol production through fermentation of sugarcane juice and its derivatives such as molasses is gaining popularity worldwide as focus shifts towards renewable energy production. However, ethanol fermentation results in the production of large volumes of a dark brown and low pH liquid waste termed vinasse. At a vinasse production rate of 12-15 liters per liter of ethanol, sustainability of this bioprocess is impacted as effluent handling costs are high. If disposed onto the land, breakdown of the organic matter within may lead to the release of greenhouse gases into the atmosphere. Additionally, disposal into water bodies results in eutrophication due to the overload of plant nutrients (N, P and K). Further, owing to the high potassium content, the use of dewatered vinasse as animal feed supplements has been shown to cause digestive tract problems in ruminants depending on the supplementation rates (>10%). To increase sustainability of bioethanol fermentation processes through combined treatment and resource recovery from vinasse, biological and physico-chemical processes have been developed and implemented in industry. Conventionally, raw vinasse is dewatered through evaporation processes (MEE) as a means of volume reduction. Membrane processes such as reverse osmosis (RO) have in the recent past become popularized as water recovery options from vinasse due to process simplicity and lower costs of equipment. Resulting concentrates from RO and MEE can be used as fertilizer. Due to the high organic content, vinasse is a suitable candidate for anaerobic digestion (AD) where the organic matter is broken down to biogas and an effluent that can be safely used as fertilizer. Additionally, the biogas from AD may be harnessed for electricity generation through combined heat and power processes or upgraded to biomethane to be used as a substitute for natural gas. For high moisture content substrates such as vinasse, up flow anaerobic sludge blanket reactors are best suited as sludge residence time is prolonged thereby increasing contact time with substrate which leading to higher methane yields. AD is often sensitive to changes in temperature, substrate composition, loading rate and pH. The presence of inhibitory components such as potassium salt ions (>11.6 g/L) in the vinasse feed result in a reduction of methanogenic activity manifested through reduced biogas and methane yields. Salt recovery processes including electrodialysis and ion-exchange have been investigated in literature on a pilot scale for the removal of K+ ions from raw vinasse. To improve resource productivity, integration of vinasse treatment processes has been implemented in industry. Integration combines biological and physico-chemical processes which results in performance optimization and energy efficiency thereby improving economic feasibility of the projects. During the project conceptualization phase, process modelling is a vital tool that can be used to predict outcomes such as substrate utilization rates, product yields and optimal operating conditions of integrated processes in a timely and cost effective manner. In addition, techno-economic analyses can be used to determine cost sensitive areas and overall feasibility of the integrated processes. Having reviewed the current industrial practices, this project sought to develop integrated flowsheets consisting of biological and physical processes for the combined vinasse treatment and value creation. Value creation was demonstrated through the recovery of valuable products including energy, salts and water from the raw vinasse. Due to its simplicity and cost effectiveness, AD was selected as the primary technology for vinasse treatment and biogas production. This was coupled with a combined heat and power system for electricity generation to form the base case flowsheet. It was hypothesized that incorporation of pre- and post-treatment as well as alternative biogas utilization processes to the base case flowsheet for recovery of salts and water would generate additional revenue and cost savings. Profitability of the base case process was expected to increase with the additional pre- and post-treatments. To fulfil the objective set out and prove the hypothesis, a three step research approach was taken. The first step involved simulation and benchmarking of the base case flowsheet (AD and CHP). Using techno-economic analyses, the effect of individual addition of pre- and post-treatment options to the base case flowsheet on profitability was investigated. A framework was then developed to investigate the incorporation of combined pre- and posttreatment options to the base case flowsheet. Thereafter, a decision support tool that in comparing various combinations of vinasse treatment routes in terms of process performance and profitability was developed to aid in the synthesis of vinasse treatment processes in industry. As bioprocess modelling is complex, it was important to select an appropriate simulation platform. Given the availability of a dedicated bioprocess compound database, sensitivity and optimization features and flexible customization options within Aspen Plus, it was preferred as the primary simulation platform over SuperPro Designer and high performance programming languages (C++, Java). In developing the base case AD flowsheet, several frameworks in the literature were considered. These included ADM1 (Batstone et al., 2002), ADM-3P (Ikumi et al., 2011) and a comprehensive model by Angelidaki et al. (1993). The presence of a well defined stoichiometric framework motivated the decision to adopt the comprehensive model by Angelidaki et al. (1993). Using a combination of in-built unit operations as well as customized user models (calculator blocks), the AD model by Angelidaki et al. (1993) was implemented on Aspen Plus. As ADM1 was considered an extension of the comprehensive model (Angelidaki et al., 1993) with several similarities, kinetic constants describing substrate uptake and microbial growth were adapted from ADM1. To ascertain the predictive quality of the built AD model, four case studies in the literature concerning the AD of manure (cow and swine) and municipal solid waste were simulated and the predicted simulation results compared to the experimental results. The developed AD model accurately predicted the methane yields of the four case studies as evidenced by the average difference of 10% between simulation and experimental results. A regression analysis between experimental and predicted data yielded a value of 0.74. Given the assumptions made in simplifying the developed model, the R2 value was deemed acceptable and further affirmed the agreement between the model and experimental results. To investigate the robustness of the developed AD model, sensitivity analyses on the feed composition as well as organic loading were conducted. Increasing inhibitory compound concentrations above certain thresholds was shown to negatively impact methanogenic activity as evidenced by the decreasing methane yields. Although ammonia is inhibitory at concentrations above 0.22 g/L, it is an important nitrogen source for biomass growth. Similarly, while acetic acid is inhibitory to acetogenic microbes, it is a crucial substrate for the growth of methanogenic archaea and methane production. Inorganic salt inhibition on the other hand may be reduced through extraction of K2SO4 through pre-treatment processes. The compositional sensitivity analyses as well as the benchmarking study showed that the built AD model had a solid core framework which accurately predicted experimental data for a range of substrates. Combined with a simplified CHP model of a Jenbacher spark ignition engine (General Electric, 2008) to form the base case flowsheet, the built AD model was used for all further simulations in this work. To determine the financial standing of the base case, simulation and subsequent techno-economic analyses were conducted. At an industrial reactor capacity of 2000 m3 and a loading rate of 25 kgCOD/m3 .day, simulation of the base case process resulted in a methane yield of 45 L-CH4/kgVSadded and an electrical production capacity of 410 kW. Discounted cash flow analyses (USD, 2016) showed that the base case was not profitable within a 20-year project lifetime as evidenced by the low return on investment and internal rate of return. However, a further sensitivity on profitability of the base case showed that decreasing potassium ion concentrations in the feed would result in higher profitability higher methane yields because of decreased K+ inhibition. Despite the positive effect of on AD performance, further analyses were required to validate feasibility of K2SO4 recovery processes as well as water recovery processes aimed at further value creation from vinasse. To investigate the effect of pre-treatment on base case flowsheet economics, an ion exchange process adapted from Zhang et al. (2012) was incorporated based on the comparatively higher degree of selectivity to K+ ions exhibited by the ion exchange process than ozonation and electrodialysis. As expected, improved CH4 yields (14%), electrical production and consequently, increases (>100%) in profitability indicators were observed. However, the pretreated base case (IEX-AD-CHP) remained unprofitable which was an indication that the marginal revenue from increased electrical production and K2SO4 sales did not match the additional capital costs. To increase profitability of the base case, biogas upgrading using a HPWS system was used in place of the CHP. Due to the comparatively low cost of HPWS equipment coupled with the increased revenue from biomethane sales, the AD-HPWS process exhibited higher profitability (ROI: 19.6%) than the base case (ROI: 0%). As evidenced by the IRR (16.3%) that was greater than the cost of capital (15%), the AD-HPWS option was profitable over a 20 year lifetime. Resource recovery from the AD effluent was sought through incorporation of RO and MEE to form the AD-CHP-RO and AD-CHP-MEE routes. Most notably, there was a significant (170%) increase in cost savings with the use of RO and MEE concentrates as fertilizer compared to the raw AD effluent from the base case. Additional cost savings of up to $27 700 were achieved with upstream reintegration of RO permeate or MEE condensate water. This savings was based on the municipal water tariff of R5/kL. The combined cost savings led to increased profitability of the base case as evidenced by the increase in ROI from 0$250 000/yr) and upstream reintegration of water. This led to the conclusion that the recovery of concentrates from vinasse is an important decision criterion when looking to increase profitability and process sustainability. Overall, based on the techno-economic analyses, the most profitable vinasse treatment process included an anaerobic digester coupled with a high-pressure water scrubbing system for biomethane production and reverse osmosis process for water recovery (ROI: 22.9%, NPV: $540 000). This facilitated both increased energy output from biomethane and cost savings from water reuse. Further research is recommended around the AD modelling aspect to extend functionality to ionic speciation and pH prediction. it is recommended that equipment quotes from suppliers within South Africa be sourced as opposed to costing heuristics in the literature to increase the accuracy of capital and operating expenditure