Artificial Intelligence and its Potential Adverse Impacts on the Philippine Economy

Recent developments in artificial intelligence (AI) and deep learning techniques are expected to reshape the nature of the working environment in many economic sectors through the automation of many white collar jobs. This technological breakthrough poses threats of job obsolescence in several industries, particularly for a labor abundant country such as the Philippines. With human capital as one of its largest resources, the services sector is a major contributor to the country’s economy, contributing around 60% of the total gross domestic product and employing about 22.8 million workers (Philippine Statistics Authority, 2017)

Optimal process capacity allocation under abnormal conditions

Plant operations may need to be optimized in response to abnormal conditions resulting from various disruptive events. In such cases, it is of interest to minimize interim economic losses by allocating the capacities of process units optimally while the plant operations deviate from the nominal design conditions. This note extends the mixed integer linear programming (MILP) model previously developed by Kasivisvanathan et al. (Applied Energy 102: 492–500, 2013) by considering the effect of financial penalties for failure to meet contractual obligations to customers; it is assumed that such penalties are paid in direct proportion to the magnitude of production deficit. The extended model is illustrated with a case study of a chlor-alkali industrial complex, and general implications of the results are discussed. © 2020, Springer Nature Singapore Pte Ltd

Optimization models for financing innovations in green energy technologies

Commercialization of emerging green technologies is essential to improve the sustainability of industrial processes. However, there are risks inherent in funding the development of new technologies that act as a significant barrier to their commercialization. Mathematical models can provide much-needed decision support to allow optimal allocation of funds, while managing the implications of techno-economic risk. The Technology Readiness Level (TRL) scale is a well-established figure of merit approach for quantifying the maturity of stand-alone technologies, while the more recently developed System Readiness Level (SRL) scale is applicable to technology networks with interdependent components. These technology maturity scales are intended mainly to be used for the passive assessment of a given state of technology, but may be incorporated within an optimization model to aid in innovation planning. In this work, two mixed integer linear programming (MILP) models are proposed to optimize strategies for funding innovation. The first model is a bi-objective MILP for optimizing the allocation of funds to a portfolio of independent innovation projects. The model is based on source-sink formulation and uses information on TRL and return on investment (ROI) to determine the best allocation of funds. The second model is a robust MILP that optimizes the allocation of limited project funds in order to maximize the SRL of a system of emerging technologies. This approach accounts for Integration Readiness Level (IRL) among mutually interdependent technologies. Both models are demonstrated with illustrative case studies on biorefinery technologies in order to demonstrate their capabilities. © 2019 Elsevier Lt

A source-sink model for optimum allocation of technology innovation portfolios

Commercialization of emerging green technologies is essential to improving the sustainability of industrial processes. In practice, it is necessary to match funding sources (e.g., research and development grants, venture capital, etc.) with projects at different maturity levels. Because of inherent uncertainties that characterize and evaluate new technologies, the decision-making process is typically fraught with risk, which can be mitigated with the use of systematic decision support methods. In this work, an optimization model is developed for optimal allocation of funds to a portfolio of innovation projects based on the available funds and different levels of technology maturity. The model is based on source-sink formulation typically used in process integration applications. Each source is a fund of known size and can only be used for projects of a specified minimum return on investment (ROI) and minimum technological readiness level (TRL); each project has an estimated cost, TRL and an ROI range across techno-economic risk scenarios. The model is formulated as a bi-objective mixed integer linear programming (MILP) model, using the conservative and optimistic total portfolio ROI as dual objective functions. The methodology is demonstrated using a pedagogical case study. Copyright © 2018, AIDIC Servizi S.r.l

Fuzzy linear programming model for the optimal design of a combined cooling, heating, and power plant

The advent of combined cooling, heating, and power (CCHP) plants introduces a new field of research and experimentation to optimize the use of such a system. A CCHP plant offers higher flexibility and efficiency, and lower greenhouse gas emissions in comparison to conventional stand-alone power production systems. As such, this paper aims to utilize actual data of power producing units to design and optimize a CCHP plant. A fuzzy mixed integer linear programming (MILP) model is proposed to select the appropriate processes to be deployed given product demand and environmental footprint constraints. The results would aid plant owners in the design of a CCHP plant. The results showed an optimal configuration of the plant consisting of a gas internal-combustion generator, a gas boiler, and a vapor absorption chiller. The environmental footprint limit was seen to be the limiting factor for the proposed optimized model to produce powers near the lower limit of the product demand constraints. © 2017 IEEE

Fuzzy multi-objective approach for designing of biomass supply chain for polygeneration with triple footprint constraints

Polygeneration systems produce multiple energy products (i.e. electricity, heat, cooling), and other biochemical products (biofuels and syngas). Such systems offer a sustainable approach in meeting the ever-growing demand of energy, while reducing its environmental impact. The optimal design of such systems should consider the design of the supply-chain in producing the targeted energy products to reduce the resource consumption and waste generation and to maximize its economic potential. One of the important considerations in designing such a system is whether to out-source its raw materials or to produce them in-house. The criteria for such decision strategies are assessed through economics, product demand, and environmental impact. One holistic way to measure the environmental impact of such system is to consider the triple footprint: carbon, water, and land. The objective of this work is to maximize the economic potential while maintaining the footprints at acceptable levels and simultaneously meeting product demands. In this study, an adoption of fuzzy multi-objective approach is presented wherein the economic potential is introduced as a constraint. Moreover, predefined fuzzy trapezoidalshaped limits for the product demand constraints are used which mimics the probabilistic demand scenario for each of the product streams. Lastly, the triple footprint constrains is utilized to assess the environmental impact of the polygeneration. The technique is demonstrated using a modified industrial case study of a polygeneration system. Copyright © 2013 by ASME